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{"title": "Power Indices for Team Reformation Planning Under Uncertainty", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3306127.3331952", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "12c539ca79d64798bccd09252d466758b8a8e4d2", "cited_by": 1, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Complexity of Control by Partition of Voters and of Voter Groups in Veto and Other Scoring Protocols", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/ntjx6933", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "1331c1411b003ada3999ff4ec63b97bbbe5fc25b", "cited_by": 8, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Extending the Range of Delivery Drones by Exploratory Learning of Energy Models", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/heen4811", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "13777061d037b41cbc3c1162b6edd62f782e64f7", "cited_by": 13, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3306127", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "13989741570c766834420985fdd170c935678a4d", "cited_by": 21, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Foresee: Attentive Future Projections of Chaotic Road Environments with Online Training", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": null, "arxiv_id": "1805.11861", "pmid": null, "openalex_id": null, "s2_id": "13add271ec46134649c72b74a852ef456aa65cd8", "cited_by": 2, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "How to Guide a Non-Cooperative Learner to Cooperate: Exploiting No-Regret Algorithms in System Design", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464126", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "13bc3053bd8c595cbce49b255687e1ba81c7f982", "cited_by": 3, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Stream Reasoning Agents: Blue Sky Ideas Track", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/wsdn8795", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "13e8f965b7730c2f5963b167b05303979cc278ce", "cited_by": 11, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Robots Reasoning with Cuts and Connections: Creating and Removing Entities (Extended Abstract)", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/smih5437", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "13ff4bad82af6391206ab6f1f76a774aa4c79e62", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Timely Information from Prediction Markets", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464085", "arxiv_id": "2011.03645", "pmid": null, "openalex_id": null, "s2_id": "1408ccc1832063322a576969b29f030cd3eef909", "cited_by": 2, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "A Need for Speed: Adapting Agent Action Speed to Improve Task Learning from Non-Expert Humans", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/dwcg3683", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "14371054fc71f64d5c93d8e503e8d813b775b09e", "cited_by": 45, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Engineering Social Agent Creation into an Opportunity for Interviewing and Interpersonal Skills Training: Socially Interactive Agents Track", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/tcfn9438", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "14400eba9bb2083a52e13f1ef4088ad4bba43ceb", "cited_by": 8, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Ethically Aligned Multi-agent Coordination to Enhance Social Welfare", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/oxfe5458", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "149daaade718fc212a3384de9f9c2a852fa3678d", "cited_by": 1, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "FRESH: Interactive Reward Shaping in High-Dimensional State Spaces using Human Feedback", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/uvld6048", "arxiv_id": "2001.06781", "pmid": null, "openalex_id": null, "s2_id": "14b7d4a34ba869e106d82d658e2973163a11eb12", "cited_by": 28, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Simplifying Urban Network Security Games with Cut-Based Graph Contraction", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/vtzk7111", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "14c6dbfbf1c8edbc6e2af5d409073748c1c35a9c", "cited_by": 15, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Poincaré-Bendixson Limit Sets in Multi-Agent Learning", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3535850.3535887", "arxiv_id": "2102.00053", "pmid": null, "openalex_id": null, "s2_id": "14cd4f782faa06c857b693e612a3068b6bc118cb", "cited_by": 6, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "ATPT: Automate Typhoon Contingency Plan Generation from Text", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464238", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "14f782711aaada0c0d38c5814e8f9d76b5063446", "cited_by": 2, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Approximating Spatial Evolutionary Games using Bayesian Networks", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464150", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "150faf626634abfee0485d0659ca88f81dda8d11", "cited_by": 2, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Computing Desirable Partitions in Coalition Formation Games", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/wrqm1012", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "153238c3880ee861844976ccc3c7bd1e40ad8323", "cited_by": 2, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS2017)", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": null, "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "155be901e253a5b393b531a457d89e0e38afe99f", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Credulous Acceptability, Poison Games and Modal Logic", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/dwyt4393", "arxiv_id": "1901.09180", "pmid": null, "openalex_id": null, "s2_id": "1564efd1ccf40086b75a070d741ef36bbd7b70a6", "cited_by": 14, "type": null, "is_oa": true, "pdf_urls": ["https://arxiv.org/pdf/1901.09180"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Computational Models of Emotion, Personality, and Social Relationships for Interactions in Games: (Extended Abstract)", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/quli2509", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "1597d4ab946eae4f9b1e2fb1a360dd75d75dd5e7", "cited_by": 23, "type": null, "is_oa": true, "pdf_urls": ["https://nottingham-repository.worktribe.com/output/773613"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Option-critic in cooperative multi-agent systems", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/iawo2403", "arxiv_id": "1911.12825", "pmid": null, "openalex_id": null, "s2_id": "15a34fbd5b8c63171c45a17fe9de94291ad28c24", "cited_by": 33, "type": null, "is_oa": true, "pdf_urls": ["https://arxiv.org/pdf/1911.12825"], "github": ["https://github.com/Jhelum-Ch/On-policy-Distributed-Option-Critic"], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Implementing Securities Based Decision Markets with Stochastic Decision Rules", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/pnmf6109", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "15a8074b94155eeac795c4b35dc3577b4a01c509", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "A Generic Platform for Training Social Skills with Adaptative Virtual Agents", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/lflc3450", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "15bf776d0073139e0003335438df8a636b356739", "cited_by": 10, "type": null, "is_oa": true, "pdf_urls": ["http://eprints.gla.ac.uk/269797/"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Learning to Design Coupons in Online Advertising Markets", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/gezs6576", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "15ddd8458b1c524139bb66119068f266e9fc5b4c", "cited_by": 6, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Multiple Assignment Problems under Lexicographic Preferences", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/ryid7073", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "15e806122dd3d2a9e2c8097ea42b565ffa32f107", "cited_by": 19, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Proportional Representation in Elections: STV vs PAV", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/rkkk5079", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "160747b9659bee27659847bd875bd96c3ed536e0", "cited_by": 7, "type": null, "is_oa": true, "pdf_urls": ["https://ruj.uj.edu.pl/xmlui/handle/item/83313"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Limitations of Greed: Influence Maximization in Undirected Networks Re-visited", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Grant Schoenebeck", "Biaoshuai Tao", "Fang-Yi Yu"], "abstract": "  We consider the influence maximization problem (selecting $k$ seeds in a\nnetwork maximizing the expected total influence) on undirected graphs under the\nlinear threshold model. On the one hand, we prove that the greedy algorithm\nalways achieves a $(1 - (1 - 1/k)^k + \\Omega(1/k^3))$-approximation, showing\nthat the greedy algorithm does slightly better on undirected graphs than the\ngeneric $(1- (1 - 1/k)^k)$ bound which also applies to directed graphs. On the\nother hand, we show that substantial improvement on this bound is impossible by\npresenting an example where the greedy algorithm can obtain at most a $(1- (1 -\n1/k)^k + O(1/k^{0.2}))$ approximation. This result stands in contrast to the\nprevious work on the independent cascade model. Like the linear threshold\nmodel, the greedy algorithm obtains a $(1-(1-1/k)^k)$-approximation on directed\ngraphs in the independent cascade model. However, Khanna and Lucier showed\nthat, in undirected graphs, the greedy algorithm performs substantially better:\na $(1-(1-1/k)^k + c)$ approximation for constant $c > 0$. Our results show\nthat, surprisingly, no such improvement occurs in the linear threshold model.\nFinally, we show that, under the linear threshold model, the approximation\nratio $(1 - (1 - 1/k)^k)$ is tight if 1) the graph is directed or 2) the\nvertices are weighted. In other words, under either of these two settings, the\ngreedy algorithm cannot achieve a $(1 - (1 - 1/k)^k + f(k))$-approximation for\nany positive function $f(k)$. The result in setting 2) is again in a sharp\ncontrast to Khanna and Lucier's $(1 - (1 - 1/k)^k + c)$-approximation result\nfor the independent cascade model, where the $(1 - (1 - 1/k)^k + c)$\napproximation guarantee can be extended to the setting where vertices are\nweighted. We also discuss extensions to more generalized settings including\nthose with edge-weighted graphs.\n", "doi": "10.5555/3398761.3398903", "arxiv_id": "2002.11679", "pmid": null, "openalex_id": null, "s2_id": "160dd80a355001dc46f5ec4956c3a4169310a399", "cited_by": 11, "type": "conference", "is_oa": true, "pdf_urls": ["https://arxiv.org/pdf/2002.11679"], "github": [], "sources": ["semanticscholar", "open-arxiv"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems", "journal_ref": "Proceedings of the 19th International Conference on Autonomous\n  Agents and MultiAgent Systems 2020, Page 1224-1232", "categories": "cs.DS cs.CC cs.SI"}
{"title": "Modeling People's Voting Behavior with Poll Information", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/srvt6467", "arxiv_id": "1909.10492", "pmid": null, "openalex_id": null, "s2_id": "161bc37d58b413b653bd0b73ec969283144027aa", "cited_by": 17, "type": null, "is_oa": true, "pdf_urls": ["https://arxiv.org/pdf/1909.10492"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Proportionally Representative Participatory Budgeting: Axioms and Algorithms", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/qhuq3213", "arxiv_id": "1711.08226", "pmid": null, "openalex_id": null, "s2_id": "162d24c06ac2e1ad383e8e920fb6c24d9e787391", "cited_by": 99, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "CrowdEval: A Cost-Efficient Strategy to Evaluate Crowdsourced Worker's Reliability", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/mmjs7280", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "166ffe983b5d67ce62b41247928ea0096efb5929", "cited_by": 14, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Towards the Verification of Strategic Properties in Multi-Agent Systems with Imperfect Information", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3545946.3598713", "arxiv_id": "2112.13621", "pmid": null, "openalex_id": null, "s2_id": "16831a395f0bc2638dbab831cfa0bbb3efe33fc7", "cited_by": 18, "type": null, "is_oa": true, "pdf_urls": ["http://arxiv.org/pdf/2112.13621"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "A Deterministic Distributed Algorithm for Reasoning with Connected Row-Convex Constraints", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/orrf9647", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "1687ec7b430de78dd4f2a58282cb870b71129a6a", "cited_by": 2, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Equitable Allocations of Indivisible Chores", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/msim4949", "arxiv_id": "2002.11504", "pmid": null, "openalex_id": null, "s2_id": "169ced4f91aad40ff7c763a11cb0957c7acc4087", "cited_by": 27, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
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{"title": "Approximate Solutions To Max-Min Fair and Proportionally Fair Allocations of Indivisible Goods", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/kvjz3488", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "16ee84aa7fd51db6fdf643d02439e2b5c0b75a6a", "cited_by": 21, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
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{"title": "Parallel Curriculum Experience Replay in Distributed Reinforcement Learning", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464046", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "3987f1cc8464ace8c9a90b3b1f82f94838ece589", "cited_by": 2, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "How to Train PointGoal Navigation Agents on a (Sample and Compute) Budget", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3535850.3536102", "arxiv_id": "2012.06117", "pmid": null, "openalex_id": null, "s2_id": "399164478dba73776d8ef5d089274225628844c8", "cited_by": 10, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Optimizing Network Structure for Preventative Health", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/beyf7766", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "39bf646eae316a44ec7e1c1eb2084098b3588a76", "cited_by": 38, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Agent-based Abstractions for Verifying Alternating-time Temporal Logic with Imperfect Information", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/pjej1962", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "39f2f5731b7d4536131a55928ce6a67acda35cce", "cited_by": 13, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Decentralised Planning for Multi-Agent Programming Platforms", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/whwc3742", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "3a03aa2d3ed269d0f1772a3d50a789f78176bb3b", "cited_by": 18, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Moving Target Defense for Web Applications using Bayesian Stackelberg Games: (Extended Abstract)", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/gsct7066", "arxiv_id": "1602.07024", "pmid": null, "openalex_id": null, "s2_id": "3a06e3ccda632988860d598c6022d1b3a288b093", "cited_by": 30, "type": null, "is_oa": false, "pdf_urls": [], "github": ["https://github.com/sailik1991/StackelbergEquilibribumSolvers"], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "eXplainable Modeling (XM): Data Analysis for Intelligent Agents", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/gajs1292", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "3a26315e2bb78d80c312ba89808a60626bc2443f", "cited_by": 6, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Applying DCOP_MST to a Team of Mobile Robots with Directional Sensing Abilities: (Extended Abstract)", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/xoxr6106", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "3a2b07072f7d8564cae1b05f96569a79bb3f081d", "cited_by": 10, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Digital Good Exchange: (Extended Abstract)", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": null, "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "3a3fa43953cfffd33c69dac0c3d4223a36351264", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Fair Allocation of Indivisible Goods to Asymmetric Agents", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.1613/jair.1.11291", "arxiv_id": "1703.01649", "pmid": null, "openalex_id": null, "s2_id": "3a4294879769d35c9dd89c2fc0331b7c766113ac", "cited_by": 117, "type": null, "is_oa": true, "pdf_urls": ["https://jair.org/index.php/jair/article/download/11291/26464"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Dynamic UAV Swarm Deployment for Non-Uniform Coverage", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/zlsf2164", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "3a46bd799cc1696b1b9d31628ac3c2fa573a895d", "cited_by": 31, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "On Decentralized Coordination for Spatial Task Allocation and Scheduling in Heterogeneous Teams", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/cyqu7826", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "3adeb01ab8ecdeb19052d9d3a6d9a3087221dc82", "cited_by": 14, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "PLOTS: Procedure Learning from Observations using Subtask Structure", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/boeo5164", "arxiv_id": "1904.09162", "pmid": null, "openalex_id": null, "s2_id": "3af4bf4c1bc05b029b05d333a4b963594727de4f", "cited_by": 3, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Pedagogical Agents as Team Members: Impact of Proactive and Pedagogical Behavior on the User", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/ssdx8875", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "3afd1c3b0344d1a42dab188101dba2dd6cdf622c", "cited_by": 6, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Warning Time: Optimizing Strategic Signaling for Security Against Boundedly Rational Adversaries", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/cqsf7535", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "3b0b64aa40aabfc49a49bb39b581974e4e74105d", "cited_by": 7, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Training Cooperative Agents for Multi-Agent Reinforcement Learning", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/izyj7724", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "3b41b32e1900c41aaaff95b47c4485dc3855bf8b", "cited_by": 4, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Group Reasoning in Social Environments", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/hrbf8813", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "3b443e041cf0769b887298ef0249261ab3d883e5", "cited_by": 12, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Do all Tournaments Admit Irrelevant Matches?", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/eagn3828", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "3b4fc8bc72e6be01f0f997d85df419b0376880b3", "cited_by": 3, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Strategy-Proof Data Auctions with Negative Externalities: (Extended Abstract)", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/2936924.2937114", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "3b65112afd2c1f51cd1bf86d9b17786a04951317", "cited_by": 6, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Robust Deep Reinforcement Learning with Adversarial Attacks", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/rnct3583", "arxiv_id": "1712.03632", "pmid": null, "openalex_id": null, "s2_id": "3b6c891fbccaa564ea4fd8914a5e3952fcf42ee3", "cited_by": 369, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Exploiting No-Regret Algorithms in System Design", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Le Cong Dinh and Nick Bishop and Long Tran-Thanh"], "abstract": "  We investigate a repeated two-player zero-sum game setting where the column\nplayer is also a designer of the system, and has full control on the design of\nthe payoff matrix. In addition, the row player uses a no-regret algorithm to\nefficiently learn how to adapt their strategy to the column player's behaviour\nover time in order to achieve good total payoff. The goal of the column player\nis to guide her opponent to pick a mixed strategy which is favourable for the\nsystem designer. Therefore, she needs to: (i) design an appropriate payoff\nmatrix $A$ whose unique minimax solution contains the desired mixed strategy of\nthe row player; and (ii) strategically interact with the row player during a\nsequence of plays in order to guide her opponent to converge to that desired\nbehaviour. To design such a payoff matrix, we propose a novel solution that\nprovably has a unique minimax solution with the desired behaviour. We also\ninvestigate a relaxation of this problem where uniqueness is not required, but\nall the minimax solutions have the same mixed strategy for the row player.\nFinally, we propose a new game playing algorithm for the system designer and\nprove that it can guide the row player, who may play a \\emph{stable} no-regret\nalgorithm, to converge to a minimax solution.\n", "doi": "10.5555/3535850.3536124", "arxiv_id": "2007.11172", "pmid": null, "openalex_id": null, "s2_id": "3b9083387a9475b72b70e34e52cce99ce9b5523f", "cited_by": 2, "type": "conference", "is_oa": true, "pdf_urls": ["http://arxiv.org/pdf/2007.11172", "https://arxiv.org/pdf/2007.11172"], "github": [], "sources": ["semanticscholar", "open-arxiv"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems", "journal_ref": "Proceedings of the 21st International Conference on Autonomous\n  Agents and Multiagent Systems 2022", "categories": "cs.GT cs.AI"}
{"title": "Decentralised Online Planning for Multi-Robot Warehouse Commissioning", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/sbjb8939", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "3bbd9ecd2e8d5496fa5e4ef83bfd2622a2c1a54f", "cited_by": 79, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "A Collaborative Platform for Identifying Context-Specific Values", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464233", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "3bdfa3311ae1474334ebae58f0c22a3226ec3369", "cited_by": 2, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Extracting Dialogical Explanations for Review Aggregations with Argumentative Dialogical Agents", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/adji8833", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "3c108058e413227a9ebd4a61621576890e9887b6", "cited_by": 68, "type": null, "is_oa": true, "pdf_urls": ["https://kclpure.kcl.ac.uk/ws/files/132187849/Extracting_Dialogical_Explanations_for_COCARASCU_Acc23Jan2019_GREEN_AAM.pdf"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Using Facial Expression and Body Language to Express Attitude for Non-Humanoid Robot: (Extended Abstract)", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/pfmx8693", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "3c1fcd6f1e2b1776e0259d964a3a24549b7e0cd9", "cited_by": 6, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "PRESS: PeRsonalized Event Scheduling recommender System (Demonstration)", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/rarw7893", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "3c23e20e35f93ed00ed5187ea9ccc2d07fcea113", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Learning an Effective Control Policy for a Robotic Drumstick via Self-Supervision", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/todl5496", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "3c38dbbd0f08653dc115e6a32dae0633ef1f7475", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Towards a Prototypical Approach to Tool-Use Improvisation", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/hznm6689", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "3c390c6bbf70a845598057916c7b57c5b4a7558d", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Maximin-Aware Allocations of Indivisible Goods", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.24963/ijcai.2019/20", "arxiv_id": "1905.09969", "pmid": null, "openalex_id": null, "s2_id": "3c4bc658ec40aa186c6132d6a65ced7e36efe73b", "cited_by": 46, "type": null, "is_oa": true, "pdf_urls": ["https://www.ijcai.org/proceedings/2019/0020.pdf"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Convergence of Multi-Agent Learning with a Finite Step Size in General-Sum Games", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/bgtg8503", "arxiv_id": "1903.02868", "pmid": null, "openalex_id": null, "s2_id": "3c79a2e67c73ba31794854009daecb782cbd2ecc", "cited_by": 14, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "An Autonomous Negotiating Agent Framework with Reinforcement Learning Based Strategies and Adaptive Strategy Switching Mechanism", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464087", "arxiv_id": "2102.03588", "pmid": null, "openalex_id": null, "s2_id": "3c7ecddcfcf87cd79fbee5dca442bddf615d5e08", "cited_by": 29, "type": null, "is_oa": true, "pdf_urls": ["http://arxiv.org/pdf/2102.03588"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Proportionality and Strategyproofness in Multiwinner Elections", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3237383.3237931", "arxiv_id": "2104.08594", "pmid": null, "openalex_id": null, "s2_id": "3cb0fca59c0196eb7f03cebaca05c76ab6e58fd9", "cited_by": 64, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Curriculum Learning in Reinforcement Learning: (Doctoral Consortium)", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/mqtp9728", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "3cbd4cb9aaad8d1a3c8658fd5cbd13ec696c44a4", "cited_by": 3, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Adversary Models Account for Imperfect Crime Data: Forecasting and Planning against Real-world Poachers", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/ghqw1780", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "3d0550ad2b0ed2193d4224e32fc54b60ecb51e05", "cited_by": 36, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Distributed Constraint Optimization for the Internet-of-Things (DCOP for IoT)", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": null, "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "3d1dfbe615dcd18438808f6a02d39a56fbeb8feb", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
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{"title": "Complexity of Additive Committee Selection with Outliers", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/bwmv7659", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "5eedb137f79ab54418b64135ee16cbb2eb073608", "cited_by": 3, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "A Local Search Based Approach to Solve Continuous DCOPs", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464083", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "5f363b6b40d5ab965f3286faa627f7b7912cf2c1", "cited_by": 16, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Intolerance does not Necessarily Lead to Segregation: A Computer-aided Analysis of the Schelling Segregation Model", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/afru5649", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "5f3ce93afa8aed522110eacef96cde5cabc5409a", "cited_by": 4, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Autonomous Agents and Multiagent Systems Challenges in Earth Observation Satellite Constellations", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3463961", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "5f6bfebc9007bd2310df2f6e315375cc92455a78", "cited_by": 10, "type": null, "is_oa": true, "pdf_urls": ["https://hal.archives-ouvertes.fr/hal-03181968/document"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2018, Stockholm, Sweden, July 10-15, 2018", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": null, "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "5f767edb9cbb10c1b25aa06054a7bebcf4038ce1", "cited_by": 4, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Playing Repeated Security Games with No Prior Knowledge", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/bsmm7354", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "5f97238612bea789463858f2ba316d352fbe92c6", "cited_by": 35, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Information Design in Crowdfunding under Thresholding Policies", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/vdxx5800", "arxiv_id": "1709.04049", "pmid": null, "openalex_id": null, "s2_id": "5fa4ece154401604bb7b48e88ec76ddb5191e1dd", "cited_by": 15, "type": null, "is_oa": true, "pdf_urls": ["https://arxiv.org/pdf/1709.04049"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "'Why didn't you allocate this task to them?' Negotiation-Aware Explicable Task Allocation and Contrastive Explanation Generation", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3545946.3598911", "arxiv_id": "2002.01640", "pmid": null, "openalex_id": null, "s2_id": "5fc4be64ef43b7ee76707d4a8786741e031c27da", "cited_by": 3, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "On Demonstrating the Impact of Defeasible Reasoning in Practice via a Multi-layer Argument-based Framework", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/iwpx2978", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "5fd21b95ea78d2370d59991cf1c499ecd88d45ce", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Empirical Game-Theoretic Analysis for Mean Field Games", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3545946.3598742", "arxiv_id": "2112.00900", "pmid": null, "openalex_id": null, "s2_id": "5fe1625072de10aa36d9d4d974fda54ede1ffa9b", "cited_by": 5, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Modeling Random Guessing and Task Difficulty for Truth Inference in Crowdsourcing", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/sexs6694", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "5fe385cbe55a128a0315d51dc50145831cbdbbfa", "cited_by": 7, "type": null, "is_oa": true, "pdf_urls": ["https://figshare.com/articles/conference_contribution/Modeling_random_guessing_and_task_difficulty_for_truth_inference_in_crowdsourcing/23153453"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "The Body is Not a Given: Joint Agent Policy Learning and Morphology Evolution", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/mfry9850", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "60207969bdffbffb2b69c37d7d25231867a8f356", "cited_by": 13, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Majority Graphs of Assignment Problems and Properties of Popular Random Assignments", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3091125.3091178", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "604c695428b734b1f3ee980ae95f14c423324484", "cited_by": 15, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Theoretical Foundations of Team Matchmaking", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/xacg1525", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "60673ac5a998858294e2d1e5f78ae2c14fb0f3ee", "cited_by": 8, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "A Generic Domain Pruning Technique for GDL-Based DCOP Algorithms in Cooperative Multi-Agent Systems", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/dzzz2008", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "60bd41fff7381a37105f6a87a28c83668648ff25", "cited_by": 15, "type": null, "is_oa": true, "pdf_urls": ["https://dl.acm.org/doi/pdf/10.5555/3237383.3237937?download=true"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Collaborative Human Task Assignment for Open Systems: (Extended Abstract)", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/ydei7799", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "60c6467aa9cbf2afd21f10bd974fe4275753a4d4", "cited_by": 2, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "A Representation Theorem for Reasoning in First-Order Multi-Agent Knowledge Bases", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/hbod4601", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "60dc53f22039b97c95629dd152cdcdf8971b5858", "cited_by": 1, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Optimal Social Decision Making", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/rnlm6816", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "60f2acf4cd1526848b3b3a7de1a38c42dc900d5a", "cited_by": 1, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "FActCheck: Keeping Activation of Fake News at Check", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/rxnc9027", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "60f872252485cf8c0949e1bc0c18ed4b5b202562", "cited_by": 2, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "K-ACE: An Innovative Multi-Component and Multi-Level Architecture", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/vxyb8133", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "61763146ffc41b041b0db7c5d103326c710ceb16", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Hierarchical Agent Supervision", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/otyn2385", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "61ad9c706cbe8fc7c8dd4fbc49265af3b6eac583", "cited_by": 6, "type": null, "is_oa": true, "pdf_urls": ["https://iris.uniroma1.it/bitstream/11573/1182869/1/Banihashemi_Postprint_Hierarchical_2018.pdf"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Implementing Business Processes in JaCaMo+ by Exploiting Accountability and Responsibility", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/iops8749", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "61cf3252dfd015b285f28818c4076c205cfce1a0", "cited_by": 3, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Exploring Improvisational Approaches to Social Knowledge Acquisition", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/jqfk1247", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "61f5c61ee33e1b749519e0fcef45cd6fce7603cf", "cited_by": 4, "type": null, "is_oa": true, "pdf_urls": ["http://eprints.gla.ac.uk/190276/1/190276.pdf"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Playing Atari with Six Neurons", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/ipby2290", "arxiv_id": "1806.01363", "pmid": null, "openalex_id": null, "s2_id": "6201fdf649a54892cce3786dc88ad0d074661243", "cited_by": 46, "type": null, "is_oa": false, "pdf_urls": [], "github": ["https://github.com/giuse/DNE"], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "\"Do As I Say, Not As I Do\": Challenges in Delegating Decisions to Automated Agents", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/ouxz2241", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "620deeb005b6b4d8a0067e1ca04056252dcea70f", "cited_by": 14, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "CopyCAT: : Taking Control of Neural Policies with Constant Attacks", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/tytx9168", "arxiv_id": "1905.12282", "pmid": null, "openalex_id": null, "s2_id": "6252044bed5824ea8ca519c71fed56a90f5a0ee2", "cited_by": 34, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Principled Autonomous Decision Making for Markets", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3091125.3091477", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "62aed6aa1936e119d31c8973331fc45eed046687", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Multiple-Profile Prediction-of-Use Games", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.1007/978-3-319-71682-4_17", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "62deec35841d4d2359e765040675df4a6b29b064", "cited_by": 1, "type": null, "is_oa": true, "pdf_urls": ["https://www.ijcai.org/proceedings/2017/0052.pdf"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Obtaining Costly Unverifiable Valuations from a Single Agent", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/jawk5655", "arxiv_id": "1804.08314", "pmid": null, "openalex_id": null, "s2_id": "62e364b041a03db76ac9543144034210e65959b4", "cited_by": 1, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Mining International Political Norms from the GDELT Database", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.1007/978-3-030-72376-7_3", "arxiv_id": "2003.14027", "pmid": null, "openalex_id": null, "s2_id": "62e87458ddb2bac163afc6eb4b2f28de46669bb1", "cited_by": 3, "type": null, "is_oa": true, "pdf_urls": ["https://arxiv.org/pdf/2003.14027"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Socially Supervised Representation Learning: The Role of Subjectivity in Learning Efficient Representations", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Julius Taylor", "Eleni Nisioti", "Cl\\'ement Moulin-Frier"], "abstract": "  Despite its rise as a prominent solution to the data inefficiency of today's\nmachine learning models, self-supervised learning has yet to be studied from a\npurely multi-agent perspective. In this work, we propose that aligning internal\nsubjective representations, which naturally arise in a multi-agent setup where\nagents receive partial observations of the same underlying environmental state,\ncan lead to more data-efficient representations. We propose that multi-agent\nenvironments, where agents do not have access to the observations of others but\ncan communicate within a limited range, guarantees a common context that can be\nleveraged in individual representation learning. The reason is that subjective\nobservations necessarily refer to the same subset of the underlying\nenvironmental states and that communication about these states can freely offer\na supervised signal. To highlight the importance of communication, we refer to\nour setting as \\textit{socially supervised representation learning}. We present\na minimal architecture comprised of a population of autoencoders, where we\ndefine loss functions, capturing different aspects of effective communication,\nand examine their effect on the learned representations. We show that our\nproposed architecture allows the emergence of aligned representations. The\nsubjectivity introduced by presenting agents with distinct perspectives of the\nenvironment state contributes to learning abstract representations that\noutperform those learned by a single autoencoder and a population of\nautoencoders, presented with identical perspectives of the environment state.\nAltogether, our results demonstrate how communication from subjective\nperspectives can lead to the acquisition of more abstract representations in\nmulti-agent systems, opening promising perspectives for future research at the\nintersection of representation learning and emergent communication.\n", "doi": "10.5555/3535850.3535992", "arxiv_id": "2109.09390", "pmid": null, "openalex_id": null, "s2_id": "62f5d55d5d44eef26fa1d99fa5f03d212b2422ac", "cited_by": 0, "type": "conference", "is_oa": true, "pdf_urls": ["http://arxiv.org/pdf/2109.09390", "https://arxiv.org/pdf/2109.09390"], "github": [], "sources": ["semanticscholar", "open-arxiv"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems", "journal_ref": "International Conference on Autonomous Agents and Multi-Agent\n  Systems (AAMAS 2022)", "categories": "cs.AI"}
{"title": "Argumentation is More Important than Appearance for Designing Culturally Tailored Virtual Agents", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/jlbq1454", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "6303bb80e7601f0fbf22886ff99c03cfb59e9cd1", "cited_by": 8, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Computing Desirable Outcomes in Specific Multi-Agent Scenarios", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464243", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "630b7fd598e0c7a8c0342336fb4890827f08f7b3", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Emergence of Cooperation in Complex Agent Networks Based on Expectation of Cooperation: (Extended Abstract)", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/wkfn7540", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "630dd0a326b075e3c0f99ed5199f500e76e8c5a5", "cited_by": 3, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "From Hotelling to Load Balancing: Approximation and the Principle of Minimum Differentiation", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/jwuy5768", "arxiv_id": "1903.04265", "pmid": null, "openalex_id": null, "s2_id": "630ed123d6dc7d8b200da5fbb4575be68b58890f", "cited_by": 10, "type": null, "is_oa": true, "pdf_urls": ["https://arxiv.org/pdf/1903.04265"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Learning Complementary Representations of the Past using Auxiliary Tasks in Partially Observable Reinforcement Learning", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/lxvu6555", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "6348b88a5160b0ef271bc0d648d2d37775ffaf85", "cited_by": 6, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
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{"title": "Simulation Summarization: (Extended Abstract)", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/schr1647", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "72f4056dd2d1440a6647396c1716e28a88c61611", "cited_by": 3, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Risk-Aware Conditional Replanning for Globally Constrained Multi-Agent Sequential Decision Making", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/bzfz4462", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "7312415ee1d51c89957291a5f54d0a7b43bd0b64", "cited_by": 5, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "A Study of Incentive Compatibility and Stability Issues in Fractional Matchings", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/jxui3105", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "733b68764e66f17e3a287a09bb4241d442cd5fab", "cited_by": 4, "type": null, "is_oa": true, "pdf_urls": ["https://arxiv.org/pdf/2001.05652"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Second-order Propositional Announcement Logic", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/tcof2574", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "7340fd5e015590b24a1941ca686476e908c03344", "cited_by": 10, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Local Wealth Redistribution Promotes Cooperation in Multiagent Systems", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/hcsq3483", "arxiv_id": "1802.01730", "pmid": null, "openalex_id": null, "s2_id": "735163f3e61214568cf323edfe90c4323205a1cb", "cited_by": 11, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Model-Driven Engineering in Agent-based Modeling and Simulation: a Case Study in the Traffic Signal Control Domain", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/kwvu7939", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "736e3a830e68aa61fc32dc6e16444045cc1e018e", "cited_by": 6, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Long-Run Multi-Robot Planning With Uncertain Task Durations", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/vfvv4451", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "73878c6059288b771f588d7aae005a76a23120c3", "cited_by": 4, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Arnor: Modeling Social Intelligence via Norms to Engineer Privacy-Aware Personal Agents", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/yiuy7084", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "7388be17e5726edb0fc5d3061a225928d73e890f", "cited_by": 19, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "An Autonomous Agent for Learning Spatiotemporal Models of Human Daily Activities: (Extended Abstract)", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/ebhr7322", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "73a508f7177c67aeb336afeb34391bfc18231340", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Sequential and Swap Mechanisms for Public Housing Allocation with Quotas and Neighbourhood-based Utilities", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.1145/3569704", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "73be710517d9fe0ed33c12cce1794f792cce82af", "cited_by": 8, "type": null, "is_oa": true, "pdf_urls": ["https://dl.acm.org/doi/pdf/10.1145/3569704"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Multi-Robot Simultaneous Coverage and Mapping of Complex Scene - Comparison of Different Strategies", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/rhjw7540", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "73c7a61d16b46d30811cec76cbfe8f0efb9fd0dd", "cited_by": 12, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Concurrent Multi-Player Parity Games", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/ihjo1992", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "7422397ed7d88d5fa30e994dca9042695010822c", "cited_by": 14, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Likelihood Quantile Networks for Coordinating Multi-Agent Reinforcement Learning", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/cjwi9118", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "74469a8da66694787da45a092006f4de6ac8ee68", "cited_by": 28, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Preference Learning in Automated Negotiation Using Gaussian Uncertainty Models", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/bgmo3711", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "745414eb904c369703bb0846c40f76e6324de5ec", "cited_by": 4, "type": null, "is_oa": true, "pdf_urls": ["https://ir.cwi.nl/pub/29011/p2087-leahu.pdf"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "A Social Choice Theoretic Perspective on Database Aggregation", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/pgmn5762", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "746001bec76eae08fe5faaf71b288bf70cb7c509", "cited_by": 1, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Towards Modeling the Interplay of Personality, Motivation, Emotion, and Mood in Social Agents", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/wwnk6060", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "74640539bef4577e09e5f7e6398db9e2a12b8944", "cited_by": 7, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Learning Cognitive Affordances for Objects from Natural Language Instruction", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": null, "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "746741ccebd1c2e0359091b1a3e9fb220c4e73b5", "cited_by": 6, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Fast Adaptation to External Agents via Meta Imitation Counterfactual Regret Advantage", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464209", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "7468e4c58f283bd9d490b9c830f58321b25f349d", "cited_by": 1, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Minimizing Margin of Victory for Fair Political and Educational Districting", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/qyyw1797", "arxiv_id": "1909.05583", "pmid": null, "openalex_id": null, "s2_id": "7480959aaf0ee15033bf7ab88c2602673f443cad", "cited_by": 18, "type": null, "is_oa": true, "pdf_urls": ["https://arxiv.org/pdf/1909.05583"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Multi-Objective Distributed Pseudo-Tree Optimization", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/icgt3154", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "748f19340313706cae5e765b3785374224e11590", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Optimal Control of Complex Systems through Variational Inference with a Discrete Event Decision Process", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/fjfs6108", "arxiv_id": "1905.02606", "pmid": null, "openalex_id": null, "s2_id": "749ec384deac4611fb13a84b8d8789d1b9227693", "cited_by": 10, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Simulation of Agent-rescuer Behaviour in Emergency Based on Modified Fuzzy Clustering: (Extended Abstract)", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/aral7191", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "74bc388c93ac09f0429d75ab4108acadb7f28b02", "cited_by": 14, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "A Knowledge Compilation Map for Conditional Preference Statements-based Languages", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464014", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "74c8b9568fbf565079b4ea76c6f6c221b28cc301", "cited_by": 3, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Explainable Agency in Intelligent Agents: Doctoral Consortium", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/hkau7467", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "74df8f66eb0f99616c828ae5a5152a7025626ab3", "cited_by": 4, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Utility Decomposition with Deep Corrections for Scalable Planning under Uncertainty", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/vzgx6220", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "74f0142b4ef581101ac0d98cc941a5326b5dc3da", "cited_by": 11, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Load Forecasting in a Smart Grid through Customer Behaviour Learning Using L1-Regularized Continuous Conditional Random Fields", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/psys8238", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "752c30543c6604c6e2b1639f680f6ecf291b3b15", "cited_by": 4, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "I Know What You Don't Know: Proactive Learning through Targeted Human Interaction", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/wevt6548", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "752ef11528ff0f03a043e7369795cc5ded22ea68", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Deep Interactive Bayesian Reinforcement Learning via Meta-Learning", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464210", "arxiv_id": "2101.03864", "pmid": null, "openalex_id": null, "s2_id": "754878242a3b480b2ca9031bff623f2c557f2caa", "cited_by": 61, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "ER-DCOPs: A Framework for Distributed Constraint Optimization with Uncertainty in Constraint Utilities", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/zhts8572", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "754b854c5b935d59d4834e86809cbdecac01d661", "cited_by": 17, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "CARPooL: Collective Adaptation using concuRrent PLanning", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/yzyr6816", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "755d21e24dd755f92363d968e4e424dc2d2d643a", "cited_by": 4, "type": null, "is_oa": true, "pdf_urls": ["http://hdl.handle.net/10230/35337"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Responsibility Research for Trustworthy Autonomous Systems", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3463964", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "755fa6a6bf0d1471582396c38af019aac4b7286a", "cited_by": 27, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Strategy RV: A Tool to Approximate ATL Model Checking under Imperfect Information and Perfect Recall", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464230", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "758c6228cad7c920147c1eb1fd125f461197ec47", "cited_by": 9, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Online Recognition of Navigation Goals Through Goal Mirroring", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/hacl5776", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "758f1817bbd019abbc34c496941c7faae1dc2945", "cited_by": 6, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Unbiased Asymmetric Reinforcement Learning under Partial Observability", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Andrea Baisero and Christopher Amato"], "abstract": "  In partially observable reinforcement learning, offline training gives access\nto latent information which is not available during online training and/or\nexecution, such as the system state. Asymmetric actor-critic methods exploit\nsuch information by training a history-based policy via a state-based critic.\nHowever, many asymmetric methods lack theoretical foundation, and are only\nevaluated on limited domains. We examine the theory of asymmetric actor-critic\nmethods which use state-based critics, and expose fundamental issues which\nundermine the validity of a common variant, and limit its ability to address\npartial observability. We propose an unbiased asymmetric actor-critic variant\nwhich is able to exploit state information while remaining theoretically sound,\nmaintaining the validity of the policy gradient theorem, and introducing no\nbias and relatively low variance into the training process. An empirical\nevaluation performed on domains which exhibit significant partial observability\nconfirms our analysis, demonstrating that unbiased asymmetric actor-critic\nconverges to better policies and/or faster than symmetric and biased asymmetric\nbaselines.\n", "doi": "10.5555/3535850.3535857", "arxiv_id": "2105.11674", "pmid": null, "openalex_id": null, "s2_id": "7590e37b1f8d5552a0c0e5b7ce54825fe5f08773", "cited_by": 34, "type": "conference", "is_oa": true, "pdf_urls": ["http://arxiv.org/pdf/2105.11674", "https://arxiv.org/pdf/2105.11674"], "github": [], "sources": ["semanticscholar", "open-arxiv"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems", "journal_ref": "Proceedings of the 21st International Conference on Autonomous\n  Agents and Multiagent Systems, pp. 44-52. 2022", "categories": "cs.LG cs.AI"}
{"title": "Requirements Specification in The Prometheus Methodology via Activity Diagrams", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/oevc7002", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "75a925e58bed05062fae2656e5b587643e368d3e", "cited_by": 2, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Coordinating the Crowd: Inducing Desirable Equilibria in Non-Cooperative Systems", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/hftv9423", "arxiv_id": "1901.10923", "pmid": null, "openalex_id": null, "s2_id": "75b49b22b211fc0f4596f991a9919320dd870042", "cited_by": 44, "type": null, "is_oa": true, "pdf_urls": ["https://dl.acm.org/doi/pdf/10.5555/3306127.3331718?download=true"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
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{"title": "On the Construction of Covert Networks", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/oahb8496", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "7619f70c21d12e1a84912ffc5e560024e61ffb66", "cited_by": 25, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
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{"title": "Traffic3D: A New Traffic Simulation Paradigm", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/pmul2995", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "a6179db556071e549c6caa094a4aad1de956607b", "cited_by": 8, "type": null, "is_oa": true, "pdf_urls": ["https://publications.aston.ac.uk/id/eprint/39573/1/AAMASdemo.pdf"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "On the Sensory Commutativity of Action Sequences for Embodied Agents", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464129", "arxiv_id": "2002.05630", "pmid": null, "openalex_id": null, "s2_id": "a64f018fc11246e7b4319a0563830a17f414f42a", "cited_by": 4, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "An Adaptive Learning Framework for Efficient Emergence of Social Norms: (Extended Abstract)", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/gait5254", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "a66aec136abd79da6b283ef21e5e1540edb45e1f", "cited_by": 8, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Transferable Environment Poisoning: Training-time Attack on Reinforcement Learning", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464113", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "a6b8b40f8045ce72fb3cdaae2d37aead4b5adf3b", "cited_by": 37, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "On a Flexible Representation for Defeasible Reasoning Variants", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/jiud5014", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "a6bfb1b817062e0176490139f9f49c5eea00afcb", "cited_by": 20, "type": null, "is_oa": true, "pdf_urls": ["https://hal.inrae.fr/hal-02736987"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Effect of Leader Placement on Robotic Swarm Control", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/ekqa5265", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "a6cab648360adbfa3e60199dc95c54418a466833", "cited_by": 9, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Incorporating social practices in BDI agent systems", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.1007/978-3-030-51417-4_6", "arxiv_id": "1903.03189", "pmid": null, "openalex_id": null, "s2_id": "a6d71b9f4b189017e8fcf9e62091e105ef2aff0d", "cited_by": 4, "type": null, "is_oa": true, "pdf_urls": ["https://arxiv.org/pdf/1903.03189"], "github": ["https://github.com/scranefield/jason-social-practices"], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Optimal Pricing for Efficient Electric Vehicle Charging Station Management", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/lojy1739", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "a6fe5d550f3ceacfb8f758ed54fa74093b472894", "cited_by": 34, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Cognitive Homeostatic Agents", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Amol Kelkar"], "abstract": "  Human brain has been used as an inspiration for building autonomous agents,\nbut it is not obvious what level of computational description of the brain one\nshould use. This has led to overly opinionated symbolic approaches and overly\nunstructured connectionist approaches. We propose that using homeostasis as the\ncomputational description provides a good compromise. Similar to how\nphysiological homeostasis is the regulation of certain homeostatic variables,\ncognition can be interpreted as the regulation of certain 'cognitive\nhomeostatic variables'. We present an outline of a Cognitive Homeostatic Agent,\nbuilt as a hierarchy of physiological and cognitive homeostatic subsystems and\ndescribe structures and processes to guide future exploration. We expect this\nto be a fruitful line of investigation towards building sophisticated\nartificial agents that can act flexibly in complex environments, and produce\nbehaviors indicating planning, thinking and feelings.\n", "doi": "10.5555/3461017.3461021", "arxiv_id": "2103.03359", "pmid": null, "openalex_id": null, "s2_id": "a70c423049c47b04acca404977f728d19c5d8305", "cited_by": 1, "type": "conference", "is_oa": true, "pdf_urls": ["http://arxiv.org/pdf/2103.03359", "https://arxiv.org/pdf/2103.03359"], "github": [], "sources": ["semanticscholar", "open-arxiv"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems", "journal_ref": "In Proc. of the 20th International Conference on Autonomous Agents\n  and Multiagent Systems (AAMAS 2021), Online, May 3-7, 2021, IFAAMAS, 5 pages", "categories": "cs.AI cs.LG cs.MA cs.NE"}
{"title": "Deep Residual Reinforcement Learning", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/icps4845", "arxiv_id": "1905.01072", "pmid": null, "openalex_id": null, "s2_id": "a76db54e4b5130ee45c224a7518e8a159f0bd843", "cited_by": 37, "type": null, "is_oa": false, "pdf_urls": [], "github": ["https://github.com/ShangtongZhang/DeepRL"], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Attacking Power Indices by Manipulating Player Reliability", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/zqzc2812", "arxiv_id": "1903.01165", "pmid": null, "openalex_id": null, "s2_id": "a7910d8ca77583c0a40c29a4ff9ff3be9c31a8c4", "cited_by": 0, "type": null, "is_oa": true, "pdf_urls": ["https://arxiv.org/pdf/1903.01165"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "From Matching with Diversity Constraints to Matching with Regional Quotas", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/kwgm7250", "arxiv_id": "2002.06748", "pmid": null, "openalex_id": null, "s2_id": "a7be3c3bd3c34c8d6322370b09ea274f7540eb18", "cited_by": 42, "type": null, "is_oa": true, "pdf_urls": ["https://unsworks.unsw.edu.au/bitstreams/8181c0bb-8eb1-4080-b78e-39d8068d71a4/download"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Evolutionary Synthesis of Stable Normative Systems", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/jmtu5869", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "a7d225de2838c78d36defa2f6ac63d3aee5dd730", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Evolving Coverage Behaviours For MAVs Using NEAT", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/rqxk8220", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "a805bad6333123b5616ed3fda0dbee75e0f60f24", "cited_by": 2, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "An Accessible Toolkit for the Creation of Socio-EmotionalAgents", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/ogxp6634", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "a8705ab7b1471ff2d76af84b7b3540e38e4c83dd", "cited_by": 9, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Integrating BDI Agents with Agent-Based Simulation Platforms: (JAAMAS Extended Abstract)", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/uhid9502", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "a8878e0f10fd8320814dcf07cb6d7afeaba8a9a2", "cited_by": 2, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Seeking Prevention of Cognitive Decline in Elders via Activity Suggestion by A Virtual Caregiver", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/jcsd3152", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "a8da7deaed8d2fd88a9e3fd007716b912b921e40", "cited_by": 11, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Random Assignment with Optional Participation", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3091125.3091177", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "a8f1861e546a4a19c7aa9aaf76e32151d905668c", "cited_by": 11, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Network", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/bejr9896", "arxiv_id": "1903.00714", "pmid": null, "openalex_id": null, "s2_id": "a8fb5da8e076d2babb057e2e99ed85bd73f71012", "cited_by": 65, "type": null, "is_oa": true, "pdf_urls": ["https://arxiv.org/pdf/1903.00714"], "github": ["https://github.com/microsoft/maro"], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "NoRML: No-Reward Meta Learning", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/elvr9404", "arxiv_id": "1903.01063", "pmid": null, "openalex_id": null, "s2_id": "a936732daa9e7d6c170d7f4b6386e2cf74ea3e0c", "cited_by": 29, "type": null, "is_oa": true, "pdf_urls": ["https://arxiv.org/pdf/1903.01063"], "github": ["https://github.com/google-research/google-research"], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "A Logic of Evaluation", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464051", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "a93c319da326a2248e6dd1dda18aa1f522e78dfe", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Towards Completing the Puzzle: Solving Open Problems for Control in Elections", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/nmez4880", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "a93e16176e3c4b05ab2416c3dd8f12bf5c1899f2", "cited_by": 4, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "An Adaptable Self-Monitoring Framework for Opaque Machines", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/xyzv9738", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "a9458a05f1d8ff8fe0590fb80abc493fa139e376", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Assigning Tasks to Workers based on Historical Data: Online Task Assignment with Two-sided Arrivals", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/cnmv6270", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "a951e151e11335b3172ce030831ec0500504d462", "cited_by": 51, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Optimal Sequential Planning for Communicative Actions: A Bayesian Approach", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/nhil3525", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "a957bd140ed53838d374938e9de40d818aeb2df7", "cited_by": 9, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Gaussian Processes as Multiagent Reward Models", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/dgae5553", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "a97e8db391e3c828d8ae355902bbc990727e90a1", "cited_by": 1, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Unsupervised Learning of Qualitative Motion Behaviours by a Mobile Robot", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/amur9322", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "aa1b3a126c3396ee914ca89d4c3bb48376ea951a", "cited_by": 24, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "A Flexible Approach for Designing Optimal Reward Functions", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/erhw1346", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "aaba71b18c0664c906e3cab872ba56dbe4b7d107", "cited_by": 10, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Learning Queuing Strategies in Human-Multi-Robot Interaction", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/kims9439", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "ab1c43ae19c4d5ff407e3029e49f22ffb0de37c3", "cited_by": 3, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Building Cities from Slime Mould, Agents and Quantum Field Theory", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/yhae3108", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "ab2d546533177cbbb16cd98eaaf2bfcc1869b3fc", "cited_by": 2, "type": null, "is_oa": true, "pdf_urls": ["http://eprints.whiterose.ac.uk/159618/1/AH_AAMAS_Building_cities_from_slime_mould__agents_and_quantum_field_theory_-3.pdf"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "RE-ORG: An Online Repositioning Guidance Agent", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/rbgm4860", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "ab7b23b319ec2804ed3c416e8b5d084c2cc3bd07", "cited_by": 0, "type": null, "is_oa": true, "pdf_urls": ["https://ink.library.smu.edu.sg/sis_research/5111"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Walrasian Equilibria in Markets with Small Demands", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464005", "arxiv_id": "2005.11796", "pmid": null, "openalex_id": null, "s2_id": "ab9576b0a82ad9bc64158ae01a1e22aeb6c59105", "cited_by": 3, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Stability and Pareto Optimality in Refugee Allocation Matchings", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/juam5953", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "abf3b65bb3fd569b45b37151b11e833fa0f86074", "cited_by": 44, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Mise-En-Scène of Narrative Action in Interactive Storytelling", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/qytw9137", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "ac0ce4619a32abc8474a6426a5ffaf8990f0690a", "cited_by": 1, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Log-time Prediction Markets for Interval Securities", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464011", "arxiv_id": "2102.07308", "pmid": null, "openalex_id": null, "s2_id": "ac18880d3f0102d34364aaddc28711be20ec688c", "cited_by": 7, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Apprenticeship Bootstrapping: Inverse Reinforcement Learning in a Multi-Skill UAV-UGV Coordination Task", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/gkye6167", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "ac2fd2227300f5145b7a6f679c251d0ec516c9c2", "cited_by": 11, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Mechanism Design for Social Law Synthesis under Incomplete Information", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/xrgp8500", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "ac3c32e1409ca54e1c066f814b62a7d9658e8c54", "cited_by": 3, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
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{"title": "Active Perception at the Architecture Level", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/vpwz6230", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c3cb58a69de3e6b8f719b7cc2fe8304f1a03bfcd", "cited_by": 2, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Evaluation of Optimization for Pedestrian Route Guidance in Real-world Crowded Scene", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/zggz2037", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c40231a2e3a27e773368cceadd1cb878022e724a", "cited_by": 9, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Minimising the Rank Aggregation Error: (Extended Abstract)", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/gjcf4903", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c46aafbc9945319e6c5e3fb18278ca9ebbbc3ea2", "cited_by": 8, "type": null, "is_oa": true, "pdf_urls": ["https://eprints.soton.ac.uk/390801/1/aamas2016_sample_extendedabstract.pdf"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Approximate Plutocratic and Egalitarian Nash Equilibria: (Extended Abstract)", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/uiwe3006", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c46ae020cc0ae05924dda0c442c75f533b836521", "cited_by": 3, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "AED: An Anytime Evolutionary DCOP Algorithm", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3398761.3398859", "arxiv_id": "1909.06254", "pmid": null, "openalex_id": null, "s2_id": "c47f8c4761b10c7df622db06f145be88c18a29c7", "cited_by": 17, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/jsrc7365", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c4e824a574d396803cf4677b7d0ad4e28ad54804", "cited_by": 932, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "PANO: Privacy Auctioning for Online Social Networks", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/npql2179", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c4e96605286c2f6738c17c4bddfaae17d24dfa7c", "cited_by": 4, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Task Coordination in Multiagent Systems", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3398761.3399073", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c4fe0769972ecc271323300d02e101439488ffe3", "cited_by": 5, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Wheeled Robots playing Chain Catch: Strategies and Evaluation (Extended Abstract)", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/wenf5827", "arxiv_id": "1602.06460", "pmid": null, "openalex_id": null, "s2_id": "c51a05d9d4178a0064feb3725b3b64d3a2c0c711", "cited_by": 2, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Reinforcement Learning in Stationary Mean-field Games", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/jvkr7103", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c5230ab55564c45b8b3c2c404f92f61e8b678fa0", "cited_by": 140, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Multirobot Symbolic Planning under Temporal Uncertainty", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3091125.3091199", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c55e68d126bae8abe53acb0c54236eba9972260d", "cited_by": 27, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Cautious Reinforcement Learning with Logical Constraints", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/rtas4039", "arxiv_id": "2002.12156", "pmid": null, "openalex_id": null, "s2_id": "c587094d6a5ede339e26b8e0d88c0eadaf73cb80", "cited_by": 90, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Deciding the Winner of a Debate Using Bipolar Argumentation", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/dbuy9524", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c5b3cf63e9d209ac377cf237ac79d1f90df13250", "cited_by": 6, "type": null, "is_oa": true, "pdf_urls": ["http://www.ifaamas.org/Proceedings/aamas2019/pdfs/p2366.pdf"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Agent Strategies for the Hide-and-Seek Game", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/obhd1795", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c5d5cb60dd2d3f453859c7d881b6c5aec0f16471", "cited_by": 6, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Hypersequential Argumentation Frameworks: An Instantiation in the Modal Logic S5", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/gvwr8141", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c5f31fc473fba72975ba85c41c0922c32a035b25", "cited_by": 3, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Prioritized Allocation of Emergency Responders based on a Continuous-Time Incident Prediction Model", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/rzdf8284", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c5fbbcdf668f4c6c91c6da26c81d721ba12f8e5d", "cited_by": 49, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Image Sequence Understanding through Narrative Sensemaking", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464124", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c5fd541711268767442e98aa6b54677be0bd0dd1", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Off-Policy Correction For Multi-Agent Reinforcement Learning", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3535850.3536106", "arxiv_id": "2111.11229", "pmid": null, "openalex_id": null, "s2_id": "c61d9219aba95593fd2fb55e5211d0e548b54332", "cited_by": 3, "type": null, "is_oa": false, "pdf_urls": [], "github": ["https://github.com/awarelab/seed_rl"], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/chrg6153", "arxiv_id": "1802.07606", "pmid": null, "openalex_id": null, "s2_id": "c63afcf8b9fc708100c2143e41abc2b363d43e11", "cited_by": 54, "type": null, "is_oa": true, "pdf_urls": ["https://arxiv.org/pdf/1802.07606"], "github": ["https://github.com/lmzintgraf/gp_pref_elicit"], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Gradual Semantics Accounting for Varied-Strength Attacks", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/vphm3831", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c6464ae7532d8891b61c951d1ad2994142f0f157", "cited_by": 36, "type": null, "is_oa": true, "pdf_urls": ["https://hal.science/hal-02325853"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Maxmin Share Fair Allocation of Indivisible Chores to Asymmetric Agents", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/oont1380", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c6466340cd3796aaf550346b612db2bf9bad6dcc", "cited_by": 11, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Recoverable Team Formation: Building Teams Resilient to Change", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/bcap5841", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c661f773a7985ead9bcffa809dc5d2f1942f67b0", "cited_by": 16, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Coalitional Games with Stochastic Characteristic Functions Defined by Private Types", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/invr2504", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c694036ab7cb94fef91a27c3d623a1b30ca1c891", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Integrating Run-Time Incidents in a Large-Scale Simulated Urban Environment", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": null, "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c6b4f7fbc8c3a1faaf5e7477976cbd7d3c217403", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Efficient Auctions with Identity-Dependent Negative Externalities", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/lqqf6430", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c6cbb711ba87675ca681b696ced39aefb631b590", "cited_by": 3, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "A Systematic Approach for Detecting Defects in Agent Designs: (Doctoral Consortium)", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/lzcr2578", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c6ef3b9a485c184e9a44f6a1ea09bc2bdd7fee31", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Culture-Based Explainable Human-Agent Deconfliction", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Alex Raymond", "Hatice Gunes", "Amanda Prorok"], "abstract": "  Law codes and regulations help organise societies for centuries, and as AI\nsystems gain more autonomy, we question how human-agent systems can operate as\npeers under the same norms, especially when resources are contended. We posit\nthat agents must be accountable and explainable by referring to which rules\njustify their decisions. The need for explanations is associated with user\nacceptance and trust. This paper's contribution is twofold: i) we propose an\nargumentation-based human-agent architecture to map human regulations into a\nculture for artificial agents with explainable behaviour. Our architecture\nleans on the notion of argumentative dialogues and generates explanations from\nthe history of such dialogues; and ii) we validate our architecture with a user\nstudy in the context of human-agent path deconfliction. Our results show that\nexplanations provide a significantly higher improvement in human performance\nwhen systems are more complex. Consequently, we argue that the criteria\ndefining the need of explanations should also consider the complexity of a\nsystem. Qualitative findings show that when rules are more complex,\nexplanations significantly reduce the perception of challenge for humans.\n", "doi": "10.5555/3398761.3398890", "arxiv_id": "1911.10098", "pmid": null, "openalex_id": null, "s2_id": "c7053b59e29f1af183b23569c402b1092d5df126", "cited_by": 21, "type": "conference", "is_oa": true, "pdf_urls": ["http://arxiv.org/pdf/1911.10098", "https://arxiv.org/pdf/1911.10098"], "github": [], "sources": ["semanticscholar", "open-arxiv"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems", "journal_ref": "AAMAS 2020: Proceedings of the 19th International Conference on\n  Autonomous Agents and MultiAgent Systems", "categories": "cs.MA cs.HC cs.LO cs.RO"}
{"title": "Proportional Representation in Vote Streams", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/sgcc9377", "arxiv_id": "1702.08862", "pmid": null, "openalex_id": null, "s2_id": "c7070880d247bb2157c076f3736adf25b18d3d55", "cited_by": 14, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Game-theoretic Modeling of Transmission Line Reinforcements with Distributed Generation: (Extended Abstract)", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/uest3629", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c75d8bf6fd963bc521a6c9908bae5d5145c52143", "cited_by": 1, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Autonomous Fishing Vessels Roving the Seas: What Multiagent Systems Have Got to Do with It", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/pfpe7412", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c772a7ca1d0aa12c785ec6a9ab966a8e04110fc3", "cited_by": 9, "type": null, "is_oa": true, "pdf_urls": ["https://hal.science/hal-01893892"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Extending Modular Semantics for Bipolar Weighted Argumentation", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.1007/978-3-030-30179-8_23", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c77dd10f333c75f8a5d2f934af2eb3970eacc4bd", "cited_by": 37, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Learning Curriculum Policies for Reinforcement Learning", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/uoaz3967", "arxiv_id": "1812.00285", "pmid": null, "openalex_id": null, "s2_id": "c821db2ca3c26c146f75c90de8cfbd6913013748", "cited_by": 100, "type": null, "is_oa": true, "pdf_urls": ["https://arxiv.org/pdf/1812.00285"], "github": ["https://github.com/holman57/ML-Education"], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Repeated Dollar Auctions: A Multi-Armed Bandit Approach", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/ifbr5983", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c84d438e52e3706c09b79faa405e26d2dad6f69b", "cited_by": 5, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Distant Truth: Bias Under Vote Distortion Costs", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/fiwd8599", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c8608f3412f6da9a5d9844c72b6636aa6f8021fb", "cited_by": 6, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "REMAX: Relational Representation for Multi-Agent Exploration", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3535850.3535977", "arxiv_id": "2008.05214", "pmid": null, "openalex_id": null, "s2_id": "c864b7a2bd0d82c1c091b9bae2a33f1ef927f6d2", "cited_by": 4, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representations", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464090", "arxiv_id": "2012.13099", "pmid": null, "openalex_id": null, "s2_id": "c8656628ca1d6ee8cf34e3c2725ce010f733e7a7", "cited_by": 4, "type": null, "is_oa": false, "pdf_urls": [], "github": ["https://github.com/microsoft/maro"], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Koh Takeuchi", "Ryo Nishida", "Hisashi Kashima", "Masaki Onishi"], "abstract": "  Crowd movement guidance has been a fascinating problem in various fields,\nsuch as easing traffic congestion in unusual events and evacuating people from\nan emergency-affected area. To grab the reins of crowds, there has been\nconsiderable demand for a decision support system that can answer a typical\nquestion: ``what will be the outcomes of each of the possible options in the\ncurrent situation. In this paper, we consider the problem of estimating the\neffects of crowd movement guidance from past data. To cope with limited amount\nof available data biased by past decision-makers, we leverage two recent\ntechniques in deep representation learning for spatial data analysis and causal\ninference. We use a spatial convolutional operator to extract effective spatial\nfeatures of crowds from a small amount of data and use balanced representation\nlearning based on the integral probability metrics to mitigate the selection\nbias and missing counterfactual outcomes. To evaluate the performance on\nestimating the treatment effects of possible guidance, we use a multi-agent\nsimulator to generate realistic data on evacuation scenarios in a crowded\ntheater, since there are no available datasets recording outcomes of all\npossible crowd movement guidance. The results of three experiments demonstrate\nthat our proposed method reduces the estimation error by at most 56% from\nstate-of-the-art methods.\n", "doi": "10.5555/3463952.3464101", "arxiv_id": "2102.03980", "pmid": null, "openalex_id": null, "s2_id": "c866d8857b66482ab2728bffe0022875cc65eee7", "cited_by": 4, "type": "conference", "is_oa": true, "pdf_urls": ["https://arxiv.org/pdf/2102.03980"], "github": ["https://github.com/koh-t/SC-CFR"], "sources": ["semanticscholar", "open-arxiv"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems", "journal_ref": "AAMAS2020", "categories": "cs.LG"}
{"title": "Avoiding Social Disappointment in Elections", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/ucid8699", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c86bc8f71183258fbf1e09923ddfa752e9c92491", "cited_by": 1, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Heterogeneous Facility Location Games", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/wuzf8079", "arxiv_id": "2005.03095", "pmid": null, "openalex_id": null, "s2_id": "c870e4ab59573b35160844339fd17f5d7e8cf7c5", "cited_by": 44, "type": null, "is_oa": true, "pdf_urls": ["https://arxiv.org/pdf/2005.03095"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3091125", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c884a4691efd7b8a32617b3ec7685fe1c897f684", "cited_by": 18, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Referral-Embedded Provision Point Mechanisms for Crowdfunding of Public Projects", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/ukrp1829", "arxiv_id": "1610.01768", "pmid": null, "openalex_id": null, "s2_id": "c940760b9ccf73ab8cc4c2af2d0b97c97f767775", "cited_by": 10, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Multi-Robot Task Allocation - Complexity and Approximation", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3463974", "arxiv_id": "2103.12370", "pmid": null, "openalex_id": null, "s2_id": "c9411d7a02408cf3fdb4408d18bac4963176c71f", "cited_by": 26, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "How Implicit Communication Emerges during Conversation Game", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/asya5812", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c9521aff93427fc25bf4b2a367f12c1272d13e95", "cited_by": 1, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "CityScope Andorra: A Multi-level Interactive and Tangible Agent-based Visualization", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/mbba6135", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c98f38d66be2b7ca72035a76fee48d91028b600c", "cited_by": 23, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Sound Algorithms in Imperfect Information Games", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464197", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c99afa8e4b15c9c9745cfefa8dbb2adfb6a384bc", "cited_by": 3, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Pooling or Sampling: Collective Dynamics for Electrical Flow Estimation", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/woif2954", "arxiv_id": "1804.06127", "pmid": null, "openalex_id": null, "s2_id": "c9a6942373d22971d0bd5acb5ebd7c5b9a47b132", "cited_by": 9, "type": null, "is_oa": true, "pdf_urls": ["http://hdl.handle.net/11573/1183557"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Partial Robustness in Team Formation: Bridging the Gap between Robustness and Resilience", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464086", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c9a7d377474ff391f3dafbee7c42398f73e0e057", "cited_by": 8, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Ethical Judgment of Agents' Behaviors in Multi-Agent Systems", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/hblm2760", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c9e3392e971c91f8abd91703a07c0c692673fc99", "cited_by": 81, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Leveraging Communication Topologies Between Learning Agents in Deep Reinforcement Learning", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/lvjf2437", "arxiv_id": "1902.06740", "pmid": null, "openalex_id": null, "s2_id": "c9e51863e30424542e1365298b587ebf04a7129d", "cited_by": 11, "type": null, "is_oa": true, "pdf_urls": ["https://arxiv.org/pdf/1902.06740"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Understanding the Role of Inequality in Creating and Sustaining the Alcohol Harm Paradox using Agent-Based Modelling", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464242", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c9f3b126406e3eec59152b5004224dc707539010", "cited_by": 1, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Multinomial HMMs for Intent Recognition in Maritime Domains", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/lsfr8708", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "c9f4f3b5c76f66a905addd62d594d30b840cfb3f", "cited_by": 2, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Anticipatory Bayesian Policy Selection for Online Adaptation of Collaborative Robots to Unknown Human Types", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/ysnx4638", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "ca3bcbf1bc742f300579dea3f4bb488dab0267e5", "cited_by": 9, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Optimal Decision Making with CP-nets and PCP-nets", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/czwz5672", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "ca4f0a7e0ce3d318aff6f21a3896de16e2539250", "cited_by": 5, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Trust Management for Composite Services in Distributed Multi-agent Systems with Indirect Ratings: (Extended Abstract)", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/zphz7214", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "ca733286b71ed4148219edd0b7dd296cfbb7bbdf", "cited_by": 2, "type": null, "is_oa": true, "pdf_urls": ["https://figshare.com/articles/conference_contribution/Trust_management_for_composite_services_in_distributed_multi-agent_systems_with_indirect_ratings/23153660"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "A Journey Among Pairs of Vertices: Computing Robots' Paths for Performing Joint Measurements", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/ayrb9633", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "ca8e5b2abace125c3a6bd47f74b420c28dd5f52c", "cited_by": 4, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Incrementally Learning Semantic Attributes through Dialogue Interaction", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/idmc5641", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "caab6304ed64c461d3376db2827058b0f9b423cc", "cited_by": 10, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Generating an Agent Taxonomy Using Topological Data Analysis", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/mtoe8753", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "cacc5ef9d9cfd03b236e13ea2df3bd07dbd8dddf", "cited_by": 2, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Stability of Generalized Two-sided Markets with Transaction Thresholds", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/pcet5186", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "cae9d90ebb72899ae836ef2accb3ac21023fba72", "cited_by": 7, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "An Optimal Algorithm for the Stochastic Bandits with Knowing Near-optimal Mean Reward", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/qskg5890", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "cb051e06e2bfcc4b8bdbe3cc8c181f33280bf092", "cited_by": 2, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "interActors: A Model for Separating Complex Communication Concerns in Multiagent Computations", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/wjjy5186", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "cb0cd88f5ffff1be340bff774a9bd764236f9565", "cited_by": 2, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Proceedings of the Sixteenth International Conference on Antonomous Agents and Multiagent Sytems", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": null, "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "cb2477faa69db05a44fc70fc6cfa5051d3d2e759", "cited_by": 1, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Anytime Multi-Agent Path Finding via Large Neighborhood Search", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464166", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "cb3f7c57be969dd5c9272aed6c06255a25f9601a", "cited_by": 139, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Memory-Based Mechanisms for Economic Agents", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/hnbo6271", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "cb4717835d4788dfd85daa98785cfe0efe161d86", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "The Seeing-Eye Robot Grand Challenge: Rethinking Automated Care", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3463959", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "cb5df5564d2ddf4edb31f0a6bd9ebd0a633c55b9", "cited_by": 17, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Graph Theoretical Properties of Logic Based Argumentation Frameworks", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/oiog7641", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "cb6daf48430ffaef16c176690288abd420bb768e", "cited_by": 3, "type": null, "is_oa": true, "pdf_urls": ["https://hal-lirmm.ccsd.cnrs.fr/lirmm-01892594"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Engineering Business Processes through Accountability and Agents", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/lysk8282", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "cb874a55c7587d00f3c772b895e30e968eacfff0", "cited_by": 7, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Single Transferable Vote: Incomplete Knowledge and Communication Issues", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/vdzd7109", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "cc07f6e4c67df7d2dd96aec2a1df921e9d4bc3ad", "cited_by": 27, "type": null, "is_oa": true, "pdf_urls": ["https://hal.science/hal-02307486"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Engineering Commitment-based Business Protocols with the 2CL Methodology: JAAMAS Extended Abstract", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/sesb6154", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "cc71ca64ee39eaa36050f3b209af4c703240868c", "cited_by": 1, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Gerrymandering Over Graphs", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/qmby3614", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "cc7a0c022ce9dd5035dddcdebe627542e380f548", "cited_by": 22, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Enhanced Learning from Multiple Demonstrations with a Flexible Two-level Structure Approach", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/vqqg4404", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "ccf403a5dccb6f32f29f5591602515e055f28f77", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Sequential Advertising Agent with Interpretable User Hidden Intents", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/xxse4058", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "cd511d859d2b7a92da024a2d1d377887c23e850c", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Partially Cooperative Multi-Agent Periodic Indivisible Resource Allocation", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3463952.3464138", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "cd54f258269fd53ea25413b386e57b197a4e478d", "cited_by": 1, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
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{"title": "New Challenges in Matching with Constraints", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/zpgh4763", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "f0d2ba5fbd03bbfd2296fe92db231bad5892122c", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Status-quo policy gradient in Multi-Agent Reinforcement Learning", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3535850.3536024", "arxiv_id": "2111.11692", "pmid": null, "openalex_id": null, "s2_id": "f0f937ab4814b9386b7dfb6cb6e19a07fd4c1005", "cited_by": 2, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Bayesian-DPOP for Continuous Distributed Constraint Optimization Problems", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/dqrw9960", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "f103881dd4dd482839aae4df6a40e4432f9d57be", "cited_by": 11, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Optimal Temporal Plan Merging", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3398761.3398861", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "f148441a645491d02e64872d35152555d80bcd8f", "cited_by": 2, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Evolving Collective Driving Behaviors", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/fiks3405", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "f19d19c69c179cf2805cd63ac8e90c44354e85c9", "cited_by": 3, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "On the Complexity of Borda Control in Single-Peaked Elections", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/rkis7240", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "f1c08ec9e0c0e572b1b7996af3e138db9306c47b", "cited_by": 17, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "An Optimization Approach for Structured Agent-Based Provider/Receiver Tasks", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/mrep5642", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "f20edf320872cddc0238bc7e81a7471be8cfef24", "cited_by": 4, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Explaining Failures Propagations in the Execution of Multi-Agent Temporal Plans", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/vtst9659", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "f229f72abff0b6ee3d9daa7c917452883abf5e4b", "cited_by": 4, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Balancing the Pain and Gain of Hobnobbing: Utility-Based Network Building over Atributed Social Networks", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/kcyn2081", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "f241a08871af9ed1b4260e72a207e540a069175e", "cited_by": 12, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "An Automated Negotiation Agent for Permission Management", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/jqbe9095", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "f247602ca85fd5d9951f82b887c0e2b69f4e1533", "cited_by": 45, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Problems in Computational Mechanism Design", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/sina6967", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "f258f1b689fc1148e656a47dbcf87100a2507675", "cited_by": 1, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "DCRAC: Deep Conditioned Recurrent Actor-Critic for Multi-Objective Partially Observable Environments", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/krwr1659", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "f25a69a981384a71f16d3312784e80a3a6369538", "cited_by": 9, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Fairness in Multiagent Resource Allocation with Dynamic and Partial Observations", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/gvai1788", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "f26c64f3d122f0436a56ec4ac8ae7a5175b9188b", "cited_by": 9, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "What to Verify for Optimal Truthful Mechanisms without Money", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/suim2315", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "f2a01fd5055550efbabb2f89c10598279bab70cd", "cited_by": 14, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Mitigating the Curse of Correlation in Security Games by Entropy Maximization", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/lpac9958", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "f2f270e6c4310eee23b0bcd985d92b4ac4ef7c98", "cited_by": 1, "type": null, "is_oa": true, "pdf_urls": ["https://arxiv.org/pdf/1703.03912"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Contingent Payment Mechanisms for Resource Utilization", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/olnf2092", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "f2fc7423b265c51b4e5bd606d2ee279a7aabfd95", "cited_by": 1, "type": null, "is_oa": true, "pdf_urls": ["https://dl.acm.org/doi/pdf/10.5555/3306127.3331722?download=true"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Bounded Policy Synthesis for POMDPs with Safe-Reachability Objectives", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/iced8892", "arxiv_id": "1801.09780", "pmid": null, "openalex_id": null, "s2_id": "f30d4528740c2a99ce2aabda34cb4c517df1b018", "cited_by": 30, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Plan Recognition in Exploratory Domains: (Doctoral Consortium)", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/nslk5433", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "f33682f36b9280a8a0aa96472595b2bf7ba19e48", "cited_by": 0, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "A Near-Optimal Node-to-Agent Mapping Heuristic for GDL-Based DCOP Algorithms in Multi-Agent Systems", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/egfm7299", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "f3391289ecd747494a67adabc62e9524b043f138", "cited_by": 17, "type": null, "is_oa": true, "pdf_urls": ["https://eprints.soton.ac.uk/420547/1/MM_Khan_L_Tran_Thanh_W_Yeoh_2018_A_Near_Optimal_Node_to_Agent_Mapping_Heuristic_for_GDL_Based_DCOP_Algorithms_in_Multi_Agent_Sy_3_.pdf"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Mechanism Design for Defense Coordination in Security Games", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/ysvd1992", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "f33d3560338da827870ec747e5d52b53bc152b2a", "cited_by": 14, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "NAKED: N-Ary Graphs from Knowledge Bases Expressed in Datalog±", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/cjtg4864", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "f341d8d8c862df113aceb5314143b461c6679f1e", "cited_by": 5, "type": null, "is_oa": true, "pdf_urls": ["https://hal.archives-ouvertes.fr/hal-02267852"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Tracking Truth by Weighting Proxies in Liquid Democracy", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.5555/3535850.3536015", "arxiv_id": "2103.09081", "pmid": null, "openalex_id": null, "s2_id": "f3b317638c518eca413ce0c53293f4a819f06047", "cited_by": 16, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "Decision Support System for Opponents Selection in Electricity Markets Bilateral Negotiations", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/dkot4379", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "f3d4c025528bd392702bb80cd48548d34a0062a4", "cited_by": 0, "type": null, "is_oa": true, "pdf_urls": ["https://zenodo.org/record/3480115"], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "On the Computational Hardness of Manipulating Pairwise Voting Rules", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": [], "abstract": null, "doi": "10.65109/xwgp7233", "arxiv_id": null, "pmid": null, "openalex_id": null, "s2_id": "f411b7054786ad722e70f1cea6f82bfc2d28eb9e", "cited_by": 6, "type": null, "is_oa": false, "pdf_urls": [], "github": [], "sources": ["semanticscholar"], "landing": null, "s2_venue": "Adaptive Agents and Multi-Agent Systems"}
{"title": "JADE, TraSMAPI and SUMO: A tool-chain for simulating traffic light\n  control", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Tiago Azevedo", "Paulo J. M. de Ara\\'ujo", "Rosaldo J. F. Rossetti", "Ana\n  Paula C. Rocha"], "abstract": "  Increased stress, fuel consumption, air pollution, accidents and delays are\nsome of the consequences of traffic congestion usually incurring in tremendous\neconomic impacts, which society aims to remedy in order to leverage a\nsustainable development. Recently, unconventional means for modeling and\ncontrolling such complex traffic systems relying on multi-agent systems have\narisen. This paper contributes to the understanding of such complex and highly\ndynamic systems by proposing an open-source tool-chain to implement\nmulti-agent-based solutions in traffic and transportation. The proposed\napproach relies on two very popular tools in both domains, with focus on\ntraffic light control. This tool-chain consists in combining JADE (Java Agent\nDEvelopment Framework), for the implementation of multi-agent systems, with\nSUMO (Simulation of Urban MObility), for the microscopic simulation of traffic\ninteractions. TraSMAPI (Traffic Simulation Manager Application Programming\nInterface) is used to combine JADE and SUMO allowing communication between\nthem. A demonstration of the concept is presented to illustrate the main\nfeatures of this tool-chain, using Q-Learning as the reinforcement learning\nmethod for each traffic light agent in a simulated network. Results demonstrate\nthe feasibility of the proposed framework as a practical means to experiment\nwith different agent-based designs of intelligent transportation solutions.\n", "doi": "10.13140/2.1.2739.4886", "arxiv_id": "1601.08154", "pdf_urls": ["https://arxiv.org/pdf/1601.08154"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "Proceedings of the 8th International Workshop on Agents in Traffic\n  and Transportation, ATT'14, held at the Thirteenth International Joint\n  Conference on Autonomous Agents and Multiagent Systems, AAMAS'14 (2014) 8-15", "categories": "cs.MA"}
{"title": "Knowledge-Enabled Robotic Agents for Shelf Replenishment in Cluttered\n  Retail Environments", "year": 2016, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Jan Winkler", "Ferenc Balint-Benczedi", "Thiemo Wiedemeyer", "Michael Beetz", "Narunas Vaskevicius", "Christian A. Mueller", "Tobias Fromm", "Andreas Birk"], "abstract": "  Autonomous robots in unstructured and dynamically changing retail\nenvironments have to master complex perception, knowledgeprocessing, and\nmanipulation tasks. To enable them to act competently, we propose a framework\nbased on three core components: (o) a knowledge-enabled perception system,\ncapable of combining diverse information sources to cope with occlusions and\nstacked objects with a variety of textures and shapes, (o) knowledge processing\nmethods produce strategies for tidying up supermarket racks, and (o) the\nnecessary manipulation skills in confined spaces to arrange objects in\nsemi-accessible rack shelves. We demonstrate our framework in an simulated\nenvironment as well as on a real shopping rack using a PR2 robot. Typical\nsupermarket products are detected and rearranged in the retail rack, tidying up\nwhat was found to be misplaced items.\n", "doi": null, "arxiv_id": "1605.04177", "pdf_urls": ["https://arxiv.org/pdf/1605.04177"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "International Conference on Autonomous Agents and Multiagent\n  Systems, 2016", "categories": "cs.RO"}
{"title": "Monotonicity axioms in approval-based multi-winner voting rules", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Luis S\\'anchez-Fern\\'andez and Jes\\'us A. Fisteus"], "abstract": "  In this paper we study several monotonicity axioms in approval-based\nmulti-winner voting rules. We consider monotonicity with respect to the support\nreceived by the winners and also monotonicity in the size of the committee.\nMonotonicity with respect to the support is studied when the set of voters does\nnot change and when new voters enter the election. For each of these two cases\nwe consider a strong and a weak version of the axiom. We observe certain\nincompatibilities between the monotonicity axioms and well-known representation\naxioms (extended/proportional justified representation) for the voting rules\nthat we analyze and provide formal proofs of incompatibility between some of\nthese axioms and perfect representation.\n", "doi": null, "arxiv_id": "1710.04246", "pdf_urls": ["https://arxiv.org/pdf/1710.04246"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "Proc. of the 18th International Conference on Autonomous Agents\n  and Multiagent Systems (AAMAS 2019)", "categories": "cs.GT"}
{"title": "Inverse Reinforcement Learning Under Noisy Observations", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Shervin Shahryari and Prashant Doshi"], "abstract": "  We consider the problem of performing inverse reinforcement learning when the\ntrajectory of the expert is not perfectly observed by the learner. Instead, a\nnoisy continuous-time observation of the trajectory is provided to the learner.\nThis problem exhibits wide-ranging applications and the specific application we\nconsider here is the scenario in which the learner seeks to penetrate a\nperimeter patrolled by a robot. The learner's field of view is limited due to\nwhich it cannot observe the patroller's complete trajectory. Instead, we allow\nthe learner to listen to the expert's movement sound, which it can also use to\nestimate the expert's state and action using an observation model. We treat the\nexpert's state and action as hidden data and present an algorithm based on\nexpectation maximization and maximum entropy principle to solve the non-linear,\nnon-convex problem. Related work considers discrete-time observations and an\nobservation model that does not include actions. In contrast, our technique\ntakes expectations over both state and action of the expert, enabling learning\neven in the presence of extreme noise and broader applications.\n", "doi": null, "arxiv_id": "1710.10116", "pdf_urls": ["https://arxiv.org/pdf/1710.10116"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "In Proceedings of the 16th Conference on Autonomous Agents and\n  MultiAgent Systems (AAMAS '17). International Foundation for Autonomous\n  Agents and Multiagent Systems, Richland, SC, 1733-1735, 2017", "categories": "cs.RO cs.AI cs.LG"}
{"title": "Reality-Aware Social Choice", "year": 2017, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Ehud Shapiro and Nimrod Talmon"], "abstract": "  Social Choice theory generalizes voting on one proposal to ranking multiple\nproposals. Yet, while a vote on a single proposal has the status quo (Reality)\nas a default, Reality has been forsaken during this generalization. Here, we\npropose to restore this default social state and to incorporate Reality\nexplicitly into Social Choice. We show that doing so gives rise to a new\ntheory, complete with its domain restrictions, voting rules with their\nReality-aware axiomatic properties, and certain game-theoretic aspects. In\nparticular, we show how Reality can be used in a principled way to break\nCondorcet cycles and develop an efficient Reality-aware Condorcet-consistent\nagenda. We then discuss several applications of Reality-Aware Social Choice.\n", "doi": null, "arxiv_id": "1710.10117", "pdf_urls": ["https://arxiv.org/pdf/1710.10117"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "Earlier version in the Proceedings of the 17th International\n  Conference on Autonomous Agents and MultiAgent Systems (AAMAS '18).\n  International Foundation for Autonomous Agents and Multiagent Systems,\n  Richland, SC, 1188-1192. 2018", "categories": "cs.CY cs.GT cs.MA"}
{"title": "Decomposition Methods with Deep Corrections for Reinforcement Learning", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Maxime Bouton", "Kyle Julian", "Alireza Nakhaei", "Kikuo Fujimura", "and Mykel\n  J. Kochenderfer"], "abstract": "  Decomposition methods have been proposed to approximate solutions to large\nsequential decision making problems. In contexts where an agent interacts with\nmultiple entities, utility decomposition can be used to separate the global\nobjective into local tasks considering each individual entity independently. An\narbitrator is then responsible for combining the individual utilities and\nselecting an action in real time to solve the global problem. Although these\ntechniques can perform well empirically, they rely on strong assumptions of\nindependence between the local tasks and sacrifice the optimality of the global\nsolution. This paper proposes an approach that improves upon such approximate\nsolutions by learning a correction term represented by a neural network. We\ndemonstrate this approach on a fisheries management problem where multiple\nboats must coordinate to maximize their catch over time as well as on a\npedestrian avoidance problem for autonomous driving. In each problem,\ndecomposition methods can scale to multiple boats or pedestrians by using\nstrategies involving one entity. We verify empirically that the proposed\ncorrection method significantly improves the decomposition method and\noutperforms a policy trained on the full scale problem without utility\ndecomposition.\n", "doi": null, "arxiv_id": "1802.01772", "pdf_urls": ["https://arxiv.org/pdf/1802.01772"], "github": ["https://github.com/sisl/AutomotivePOMDPs.jl"], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "Journal of Agents and Multi-Agent Systems (JAAMAS), 2019", "categories": "cs.LG cs.AI"}
{"title": "Goal Inference Improves Objective and Perceived Performance in\n  Human-Robot Collaboration", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Chang Liu", "Jessica B. Hamrick", "Jaime F. Fisac", "Anca D. Dragan", "J. Karl\n  Hedrick", "S. Shankar Sastry", "Thomas L. Griffiths"], "abstract": "  The study of human-robot interaction is fundamental to the design and use of\nrobotics in real-world applications. Robots will need to predict and adapt to\nthe actions of human collaborators in order to achieve good performance and\nimprove safety and end-user adoption. This paper evaluates a human-robot\ncollaboration scheme that combines the task allocation and motion levels of\nreasoning: the robotic agent uses Bayesian inference to predict the next goal\nof its human partner from his or her ongoing motion, and re-plans its own\nactions in real time. This anticipative adaptation is desirable in many\npractical scenarios, where humans are unable or unwilling to take on the\ncognitive overhead required to explicitly communicate their intent to the\nrobot. A behavioral experiment indicates that the combination of goal inference\nand dynamic task planning significantly improves both objective and perceived\nperformance of the human-robot team. Participants were highly sensitive to the\ndifferences between robot behaviors, preferring to work with a robot that\nadapted to their actions over one that did not.\n", "doi": null, "arxiv_id": "1802.01780", "pdf_urls": ["https://arxiv.org/pdf/1802.01780"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "C. Liu, J. Hamrick, J. Fisac, A. Dragan, J. K. Hedrick, S. Sastry,\n  T. Griffiths. \"Goal Inference Improves Objective and Perceived Performance in\n  Human-Robot Collaboration\". Autonomous Agents and Multiagent Systems (AAMAS),\n  2016", "categories": "cs.RO cs.AI cs.HC"}
{"title": "Pooling or Sampling: Collective Dynamics for Electrical Flow Estimation", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Luca Becchetti", "Vincenzo Bonifaci", "Emanuele Natale"], "abstract": "  The computation of electrical flows is a crucial primitive for many recently\nproposed optimization algorithms on weighted networks. While typically\nimplemented as a centralized subroutine, the ability to perform this task in a\nfully decentralized way is implicit in a number of biological systems. Thus, a\nnatural question is whether this task can provably be accomplished in an\nefficient way by a network of agents executing a simple protocol.\n  We provide a positive answer, proposing two distributed approaches to\nelectrical flow computation on a weighted network: a deterministic process\nmimicking Jacobi's iterative method for solving linear systems, and a\nrandomized token diffusion process, based on revisiting a classical random walk\nprocess on a graph with an absorbing node. We show that both processes converge\nto a solution of Kirchhoff's node potential equations, derive bounds on their\nconvergence rates in terms of the weights of the network, and analyze their\ntime and message complexity.\n", "doi": null, "arxiv_id": "1804.06127", "pdf_urls": ["https://arxiv.org/pdf/1804.06127"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "Proceedings of the 17th International Conference on Autonomous\n  Agents and MultiAgent Systems, AAMAS 2018", "categories": "cs.DC"}
{"title": "One-Shot Learning using Mixture of Variational Autoencoders: a\n  Generalization Learning approach", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Decebal Constantin Mocanu and Elena Mocanu"], "abstract": "  Deep learning, even if it is very successful nowadays, traditionally needs\nvery large amounts of labeled data to perform excellent on the classification\ntask. In an attempt to solve this problem, the one-shot learning paradigm,\nwhich makes use of just one labeled sample per class and prior knowledge,\nbecomes increasingly important. In this paper, we propose a new one-shot\nlearning method, dubbed MoVAE (Mixture of Variational AutoEncoders), to perform\nclassification. Complementary to prior studies, MoVAE represents a shift of\nparadigm in comparison with the usual one-shot learning methods, as it does not\nuse any prior knowledge. Instead, it starts from zero knowledge and one labeled\nsample per class. Afterward, by using unlabeled data and the generalization\nlearning concept (in a way, more as humans do), it is capable to gradually\nimprove by itself its performance. Even more, if there are no unlabeled data\navailable MoVAE can still perform well in one-shot learning classification. We\ndemonstrate empirically the efficiency of our proposed approach on three\ndatasets, i.e. the handwritten digits (MNIST), fashion products\n(Fashion-MNIST), and handwritten characters (Omniglot), showing that MoVAE\noutperforms state-of-the-art one-shot learning algorithms.\n", "doi": null, "arxiv_id": "1804.07645", "pdf_urls": ["https://arxiv.org/pdf/1804.07645"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "17th International Conference on Autonomous Agents and Multiagent\n  Systems (AAMAS 2018)", "categories": "cs.CV cs.LG stat.ML"}
{"title": "Online Abstraction with MDP Homomorphisms for Deep Learning", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Ondrej Biza and Robert Platt"], "abstract": "  Abstraction of Markov Decision Processes is a useful tool for solving complex\nproblems, as it can ignore unimportant aspects of an environment, simplifying\nthe process of learning an optimal policy. In this paper, we propose a new\nalgorithm for finding abstract MDPs in environments with continuous state\nspaces. It is based on MDP homomorphisms, a structure-preserving mapping\nbetween MDPs. We demonstrate our algorithm's ability to learn abstractions from\ncollected experience and show how to reuse the abstractions to guide\nexploration in new tasks the agent encounters. Our novel task transfer method\noutperforms baselines based on a deep Q-network in the majority of our\nexperiments. The source code is at https://github.com/ondrejba/aamas_19.\n", "doi": null, "arxiv_id": "1811.12929", "pdf_urls": ["https://arxiv.org/pdf/1811.12929"], "github": ["https://github.com/ondrejba/aamas_19"], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "Proceedings of the 18th International Conference on Autonomous\n  Agents and MultiAgent Systems (AAMAS '19). 2019. 1125 - 1133", "categories": "cs.LG stat.ML"}
{"title": "Learning Curriculum Policies for Reinforcement Learning", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Sanmit Narvekar and Peter Stone"], "abstract": "  Curriculum learning in reinforcement learning is a training methodology that\nseeks to speed up learning of a difficult target task, by first training on a\nseries of simpler tasks and transferring the knowledge acquired to the target\ntask. Automatically choosing a sequence of such tasks (i.e. a curriculum) is an\nopen problem that has been the subject of much recent work in this area. In\nthis paper, we build upon a recent method for curriculum design, which\nformulates the curriculum sequencing problem as a Markov Decision Process. We\nextend this model to handle multiple transfer learning algorithms, and show for\nthe first time that a curriculum policy over this MDP can be learned from\nexperience. We explore various representations that make this possible, and\nevaluate our approach by learning curriculum policies for multiple agents in\ntwo different domains. The results show that our method produces curricula that\ncan train agents to perform on a target task as fast or faster than existing\nmethods.\n", "doi": null, "arxiv_id": "1812.00285", "pdf_urls": ["https://arxiv.org/pdf/1812.00285"], "github": ["https://github.com/holman57/ML-Education"], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "Proceedings of the 18th International Conference on Autonomous\n  Agents and Multiagent Systems (AAMAS 2019)", "categories": "cs.LG cs.AI stat.ML"}
{"title": "Likelihood Quantile Networks for Coordinating Multi-Agent Reinforcement\n  Learning", "year": 2018, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Xueguang Lyu", "Christopher Amato"], "abstract": "  When multiple agents learn in a decentralized manner, the environment appears\nnon-stationary from the perspective of an individual agent due to the\nexploration and learning of the other agents. Recently proposed deep\nmulti-agent reinforcement learning methods have tried to mitigate this\nnon-stationarity by attempting to determine which samples are from other agent\nexploration or suboptimality and take them less into account during learning.\nBased on the same philosophy, this paper introduces a decentralized quantile\nestimator, which aims to improve performance by distinguishing non-stationary\nsamples based on the likelihood of returns. In particular, each agent considers\nthe likelihood that other agent exploration and policy changes are occurring,\nessentially utilizing the agent's own estimations to weigh the learning rate\nthat should be applied towards the given samples. We introduce a formal method\nof calculating differences of our return distribution representations and\nmethods for utilizing it to guide updates. We also explore the effect of\nrisk-seeking strategies for adjusting learning over time and propose adaptive\nrisk distortion functions which guides risk sensitivity. Our experiments, on\ntraditional benchmarks and new domains, show our methods are more stable,\nsample efficient and more likely to converge to a joint optimal policy than\nprevious methods.\n", "doi": null, "arxiv_id": "1812.06319", "pdf_urls": ["https://arxiv.org/pdf/1812.06319"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "Proceedings of the 19th International Conference on Autonomous\n  Agents and MultiAgent Systems, pp. 798-806. 2020", "categories": "cs.LG stat.ML"}
{"title": "Leveraging Communication Topologies Between Learning Agents in Deep\n  Reinforcement Learning", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Dhaval Adjodah", "Dan Calacci", "Abhimanyu Dubey", "Anirudh Goyal", "Peter\n  Krafft", "Esteban Moro", "Alex Pentland"], "abstract": "  A common technique to improve learning performance in deep reinforcement\nlearning (DRL) and many other machine learning algorithms is to run multiple\nlearning agents in parallel. A neglected component in the development of these\nalgorithms has been how best to arrange the learning agents involved to improve\ndistributed search. Here we draw upon results from the networked optimization\nliteratures suggesting that arranging learning agents in communication networks\nother than fully connected topologies (the implicit way agents are commonly\narranged in) can improve learning. We explore the relative performance of four\npopular families of graphs and observe that one such family (Erdos-Renyi random\ngraphs) empirically outperforms the de facto fully-connected communication\ntopology across several DRL benchmark tasks. Additionally, we observe that 1000\nlearning agents arranged in an Erdos-Renyi graph can perform as well as 3000\nagents arranged in the standard fully-connected topology, showing the large\nlearning improvement possible when carefully designing the topology over which\nagents communicate. We complement these empirical results with a theoretical\ninvestigation of why our alternate topologies perform better. Overall, our work\nsuggests that distributed machine learning algorithms could be made more\neffective if the communication topology between learning agents was optimized.\n", "doi": null, "arxiv_id": "1902.06740", "pdf_urls": ["https://arxiv.org/pdf/1902.06740"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "AAMAS 2020", "categories": "cs.LG stat.ML"}
{"title": "Using Causal Analysis to Learn Specifications from Task Demonstrations", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Daniel Angelov", "Yordan Hristov", "Subramanian Ramamoorthy"], "abstract": "  Learning models of user behaviour is an important problem that is broadly\napplicable across many application domains requiring human-robot interaction.\nIn this work we show that it is possible to learn a generative model for\ndistinct user behavioral types, extracted from human demonstrations, by\nenforcing clustering of preferred task solutions within the latent space. We\nuse this model to differentiate between user types and to find cases with\noverlapping solutions. Moreover, we can alter an initially guessed solution to\nsatisfy the preferences that constitute a particular user type by\nbackpropagating through the learned differentiable model. An advantage of\nstructuring generative models in this way is that it allows us to extract\ncausal relationships between symbols that might form part of the user's\nspecification of the task, as manifested in the demonstrations. We show that\nthe proposed method is capable of correctly distinguishing between three user\ntypes, who differ in degrees of cautiousness in their motion, while performing\nthe task of moving objects with a kinesthetically driven robot in a tabletop\nenvironment. Our method successfully identifies the correct type, within the\nspecified time, in 99% [97.8 - 99.8] of the cases, which outperforms an IRL\nbaseline. We also show that our proposed method correctly changes a default\ntrajectory to one satisfying a particular user specification even with unseen\nobjects. The resulting trajectory is shown to be directly implementable on a\nPR2 humanoid robot completing the same task.\n", "doi": null, "arxiv_id": "1903.01267", "pdf_urls": ["https://arxiv.org/pdf/1903.01267"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "Proceedings of the 18th International Conference on Autonomous\n  Agents and MultiAgent Systems, Pages 1341-1349, Montreal QC, Canada, May 13 -\n  17, 2019", "categories": "cs.AI cs.LG"}
{"title": "MGpi: A Computational Model of Multiagent Group Perception and\n  Interaction", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Navyata Sanghvi", "Ryo Yonetani", "Kris Kitani"], "abstract": "  Toward enabling next-generation robots capable of socially intelligent\ninteraction with humans, we present a $\\mathbf{computational\\; model}$ of\ninteractions in a social environment of multiple agents and multiple groups.\nThe Multiagent Group Perception and Interaction (MGpi) network is a deep neural\nnetwork that predicts the appropriate social action to execute in a group\nconversation (e.g., speak, listen, respond, leave), taking into account\nneighbors' observable features (e.g., location of people, gaze orientation,\ndistraction, etc.). A central component of MGpi is the Kinesic-Proxemic-Message\n(KPM) gate, that performs social signal gating to extract important information\nfrom a group conversation. In particular, KPM gate filters incoming social cues\nfrom nearby agents by observing their body gestures (kinesics) and spatial\nbehavior (proxemics). The MGpi network and its KPM gate are learned via\nimitation learning, using demonstrations from our designed $\\mathbf{social\\;\ninteraction\\; simulator}$. Further, we demonstrate the efficacy of the KPM gate\nas a social attention mechanism, achieving state-of-the-art performance on the\ntask of $\\mathbf{group\\; identification}$ without using explicit group\nannotations, layout assumptions, or manually chosen parameters.\n", "doi": null, "arxiv_id": "1903.01537", "pdf_urls": ["https://arxiv.org/pdf/1903.01537"], "github": ["https://github.com/navysanghvi/MGpi"], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "Proceedings of the 19th International Conference on Autonomous\n  Agents and Multiagent Systems (AAMAS 2020), pp. 1196-1205", "categories": "cs.LG cs.AI cs.MA stat.ML"}
{"title": "Model Primitive Hierarchical Lifelong Reinforcement Learning", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Bohan Wu", "Jayesh K. Gupta", "Mykel J. Kochenderfer"], "abstract": "  Learning interpretable and transferable subpolicies and performing task\ndecomposition from a single, complex task is difficult. Some traditional\nhierarchical reinforcement learning techniques enforce this decomposition in a\ntop-down manner, while meta-learning techniques require a task distribution at\nhand to learn such decompositions. This paper presents a framework for using\ndiverse suboptimal world models to decompose complex task solutions into\nsimpler modular subpolicies. This framework performs automatic decomposition of\na single source task in a bottom up manner, concurrently learning the required\nmodular subpolicies as well as a controller to coordinate them. We perform a\nseries of experiments on high dimensional continuous action control tasks to\ndemonstrate the effectiveness of this approach at both complex single task\nlearning and lifelong learning. Finally, we perform ablation studies to\nunderstand the importance and robustness of different elements in the framework\nand limitations to this approach.\n", "doi": null, "arxiv_id": "1903.01567", "pdf_urls": ["https://arxiv.org/pdf/1903.01567"], "github": ["https://github.com/sisl/MPHRL"], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "International Conference on Autonomous Agents and Multiagent\n  Systems (AAMAS 2019)", "categories": "cs.LG cs.AI cs.NE"}
{"title": "Resource Abstraction for Reinforcement Learning in Multiagent Congestion\n  Problems", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Kleanthis Malialis and Sam Devlin and Daniel Kudenko"], "abstract": "  Real-world congestion problems (e.g. traffic congestion) are typically very\ncomplex and large-scale. Multiagent reinforcement learning (MARL) is a\npromising candidate for dealing with this emerging complexity by providing an\nautonomous and distributed solution to these problems. However, there are three\nlimiting factors that affect the deployability of MARL approaches to congestion\nproblems. These are learning time, scalability and decentralised coordination\ni.e. no communication between the learning agents. In this paper we introduce\nResource Abstraction, an approach that addresses these challenges by allocating\nthe available resources into abstract groups. This abstraction creates new\nreward functions that provide a more informative signal to the learning agents\nand aid the coordination amongst them. Experimental work is conducted on two\nbenchmark domains from the literature, an abstract congestion problem and a\nrealistic traffic congestion problem. The current state-of-the-art for solving\nmultiagent congestion problems is a form of reward shaping called difference\nrewards. We show that the system using Resource Abstraction significantly\nimproves the learning speed and scalability, and achieves the highest possible\nor near-highest joint performance/social welfare for both congestion problems\nin large-scale scenarios involving up to 1000 reinforcement learning agents.\n", "doi": null, "arxiv_id": "1903.05431", "pdf_urls": ["https://arxiv.org/pdf/1903.05431"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "Proceedings of the International Conference on Autonomous Agents\n  and Multiagent Systems (AAMAS), 2016/", "categories": "cs.MA cs.LG"}
{"title": "MCTS-based Automated Negotiation Agent (Extended Abstract)", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["C\\'edric Buron", "Zahia Guessoum (SMA)", "Sylvain Ductor (UECE)"], "abstract": "  This paper introduces a new Negotiating Agent for automated negotiation on\ncontinuous domains and without considering a specified deadline. The agent\nbidding strategy relies on Monte Carlo Tree Search, which is a trendy method\nsince it has been used with success on games with high branching factor such as\nGo. It uses two opponent modeling techniques for its bidding strategy and its\nutility: Gaussian process regression and Bayesian learning. Evaluation is done\nby confronting the existing agents that are able to negotiate in such context:\nRandom Walker, Tit-for-tat and Nice Tit-for-Tat. None of those agents succeeds\nin beating our agent; moreover the modular and adaptive nature of our approach\nis a huge advantage when it comes to optimize it in specific applicative\ncontexts.\n", "doi": null, "arxiv_id": "1903.12411", "pdf_urls": ["https://arxiv.org/pdf/1903.12411"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "AAMAS 2019, May 2019, Montreal, Canada", "categories": "cs.AI cs.MA"}
{"title": "New Algorithms for Functional Distributed Constraint Optimization\n  Problems", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Khoi D. Hoang", "William Yeoh", "Makoto Yokoo", "Zinovi Rabinovich"], "abstract": "  The Distributed Constraint Optimization Problem (DCOP) formulation is a\npowerful tool to model multi-agent coordination problems that are distributed\nby nature. The formulation is suitable for problems where variables are\ndiscrete and constraint utilities are represented in tabular form. However,\nmany real-world applications have variables that are continuous and tabular\nforms thus cannot accurately represent constraint utilities. To overcome this\nlimitation, researchers have proposed the Functional DCOP (F-DCOP) model, which\nare DCOPs with continuous variables. But existing approaches usually come with\nsome restrictions on the form of constraint utilities and are without quality\nguarantees. Therefore, in this paper, we (i) propose exact algorithms to solve\na specific subclass of F-DCOPs; (ii) propose approximation methods with quality\nguarantees to solve general F-DCOPs; and (iii) empirically show that our\nalgorithms outperform existing state-of-the-art F-DCOP algorithms on randomly\ngenerated instances when given the same communication limitations.\n", "doi": "10.5555/3398761.3398823", "arxiv_id": "1905.13275", "pdf_urls": ["https://arxiv.org/pdf/1905.13275"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "Proceedings of the 19th International Conference on Autonomous\n  Agents and MultiAgent Systems, 2020", "categories": "cs.MA"}
{"title": "A Generic Metaheuristic Approach to Sequential Security Games", "year": 2019, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Adam \\.Zychowski and Jacek Ma\\'ndziuk"], "abstract": "  The paper introduces a generic approach to solving Sequential Security Games\n(SGs) which utilizes Evolutionary Algorithms. Formulation of the method (named\nEASG) is general and largely game-independent, which allows for its application\nto a wide range of SGs with just little adjustments addressing game\nspecificity. Comprehensive experiments performed on 3 different types of games\n(with 300 instances in total) demonstrate robustness and stability of EASG,\nmanifested by repeatable achieving optimal or near-optimal solutions in the\nvast majority of the cases. The main advantage of EASG is time efficiency. The\nmethod scales visibly better than state-of-the-art approaches and consequently\ncan be applied to SG instances which are beyond capabilities of the existing\nmethods. Furthermore, due to anytime characteristics, EASG is very well suited\nfor time-critical applications, as the method can be terminated at any moment\nand still provide a reasonably good solution - the best one found so far.\n", "doi": null, "arxiv_id": "1911.05706", "pdf_urls": ["https://arxiv.org/pdf/1911.05706"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "International Conference on Autonomous Agents and Multiagent\n  Systems, AAMAS 2020, 2089-2091, ISBN: 978-1-4503-7518-4", "categories": "cs.GT"}
{"title": "Distributed Reinforcement Learning for Cooperative Multi-Robot Object\n  Manipulation", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Guohui Ding", "Joewie J. Koh", "Kelly Merckaert", "Bram Vanderborght", "Marco\n  M. Nicotra", "Christoffer Heckman", "Alessandro Roncone", "Lijun Chen"], "abstract": "  We consider solving a cooperative multi-robot object manipulation task using\nreinforcement learning (RL). We propose two distributed multi-agent RL\napproaches: distributed approximate RL (DA-RL), where each agent applies\nQ-learning with individual reward functions; and game-theoretic RL (GT-RL),\nwhere the agents update their Q-values based on the Nash equilibrium of a\nbimatrix Q-value game. We validate the proposed approaches in the setting of\ncooperative object manipulation with two simulated robot arms. Although we\nfocus on a small system of two agents in this paper, both DA-RL and GT-RL apply\nto general multi-agent systems, and are expected to scale well to large\nsystems.\n", "doi": null, "arxiv_id": "2003.09540", "pdf_urls": ["https://arxiv.org/pdf/2003.09540"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "Proceedings of the 19th International Conference on Autonomous\n  Agents and Multiagent Systems (AAMAS), 2020, pp. 1831-1833", "categories": "cs.RO cs.GT cs.LG cs.MA"}
{"title": "Automated Configuration of Negotiation Strategies", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Bram M. Renting (1)", "Holger H. Hoos (2)", "Catholijn M. Jonker (1 and 2)\n  ((1) Delft University of Technology", "(2) Leiden University)"], "abstract": "  Bidding and acceptance strategies have a substantial impact on the outcome of\nnegotiations in scenarios with linear additive and nonlinear utility functions.\nOver the years, it has become clear that there is no single best strategy for\nall negotiation settings, yet many fixed strategies are still being developed.\nWe envision a shift in the strategy design question from: What is a good\nstrategy?, towards: What could be a good strategy? For this purpose, we\ndeveloped a method leveraging automated algorithm configuration to find the\nbest strategies for a specific set of negotiation settings. By empowering\nautomated negotiating agents using automated algorithm configuration, we obtain\na flexible negotiation agent that can be configured automatically for a rich\nspace of opponents and negotiation scenarios.\n  To critically assess our approach, the agent was tested in an ANAC-like\nbilateral automated negotiation tournament setting against past competitors. We\nshow that our automatically configured agent outperforms all other agents, with\na 5.1% increase in negotiation payoff compared to the next-best agent. We note\nthat without our agent in the tournament, the top-ranked agent wins by a margin\nof only 0.01%.\n", "doi": null, "arxiv_id": "2004.00094", "pdf_urls": ["https://arxiv.org/pdf/2004.00094"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "http://ifaamas.org/Proceedings/aamas2020/pdfs/p1116.pdf", "categories": "cs.MA cs.AI cs.LG"}
{"title": "Maximizing Information Gain in Partially Observable Environments via\n  Prediction Reward", "year": 2020, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Yash Satsangi", "Sungsu Lim", "Shimon Whiteson", "Frans Oliehoek", "Martha\n  White"], "abstract": "  Information gathering in a partially observable environment can be formulated\nas a reinforcement learning (RL), problem where the reward depends on the\nagent's uncertainty. For example, the reward can be the negative entropy of the\nagent's belief over an unknown (or hidden) variable. Typically, the rewards of\nan RL agent are defined as a function of the state-action pairs and not as a\nfunction of the belief of the agent; this hinders the direct application of\ndeep RL methods for such tasks. This paper tackles the challenge of using\nbelief-based rewards for a deep RL agent, by offering a simple insight that\nmaximizing any convex function of the belief of the agent can be approximated\nby instead maximizing a prediction reward: a reward based on prediction\naccuracy. In particular, we derive the exact error between negative entropy and\nthe expected prediction reward. This insight provides theoretical motivation\nfor several fields using prediction rewards---namely visual attention, question\nanswering systems, and intrinsic motivation---and highlights their connection\nto the usually distinct fields of active perception, active sensing, and sensor\nplacement. Based on this insight we present deep anticipatory networks (DANs),\nwhich enables an agent to take actions to reduce its uncertainty without\nperforming explicit belief inference. We present two applications of DANs:\nbuilding a sensor selection system for tracking people in a shopping mall and\nlearning discrete models of attention on fashion MNIST and MNIST digit\nclassification.\n", "doi": null, "arxiv_id": "2005.04912", "pdf_urls": ["https://arxiv.org/pdf/2005.04912"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "AAMAS 2020", "categories": "cs.AI cs.LG"}
{"title": "Action Priors for Large Action Spaces in Robotics", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Ondrej Biza", "Dian Wang", "Robert Platt", "Jan-Willem van de Meent and\n  Lawson L. S. Wong"], "abstract": "  In robotics, it is often not possible to learn useful policies using pure\nmodel-free reinforcement learning without significant reward shaping or\ncurriculum learning. As a consequence, many researchers rely on expert\ndemonstrations to guide learning. However, acquiring expert demonstrations can\nbe expensive. This paper proposes an alternative approach where the solutions\nof previously solved tasks are used to produce an action prior that can\nfacilitate exploration in future tasks. The action prior is a probability\ndistribution over actions that summarizes the set of policies found solving\nprevious tasks. Our results indicate that this approach can be used to solve\nrobotic manipulation problems that would otherwise be infeasible without expert\ndemonstrations. Source code is available at\n\\url{https://github.com/ondrejba/action_priors}.\n", "doi": null, "arxiv_id": "2101.04178", "pdf_urls": ["https://arxiv.org/pdf/2101.04178"], "github": ["https://github.com/ondrejba/action_priors"], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "Proceedings of the 20th International Conference on Autonomous\n  Agents and MultiAgent Systems (AAMAS '21). 2021. 205 - 213", "categories": "cs.RO cs.LG"}
{"title": "Self-Attention Meta-Learner for Continual Learning", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Ghada Sokar", "Decebal Constantin Mocanu", "Mykola Pechenizkiy"], "abstract": "  Continual learning aims to provide intelligent agents capable of learning\nmultiple tasks sequentially with neural networks. One of its main challenging,\ncatastrophic forgetting, is caused by the neural networks non-optimal ability\nto learn in non-stationary distributions. In most settings of the current\napproaches, the agent starts from randomly initialized parameters and is\noptimized to master the current task regardless of the usefulness of the\nlearned representation for future tasks. Moreover, each of the future tasks\nuses all the previously learned knowledge although parts of this knowledge\nmight not be helpful for its learning. These cause interference among tasks,\nespecially when the data of previous tasks is not accessible. In this paper, we\npropose a new method, named Self-Attention Meta-Learner (SAM), which learns a\nprior knowledge for continual learning that permits learning a sequence of\ntasks, while avoiding catastrophic forgetting. SAM incorporates an attention\nmechanism that learns to select the particular relevant representation for each\nfuture task. Each task builds a specific representation branch on top of the\nselected knowledge, avoiding the interference between tasks. We evaluate the\nproposed method on the Split CIFAR-10/100 and Split MNIST benchmarks in the\ntask agnostic inference. We empirically show that we can achieve a better\nperformance than several state-of-the-art methods for continual learning by\nbuilding on the top of selected representation learned by SAM. We also show the\nrole of the meta-attention mechanism in boosting informative features\ncorresponding to the input data and identifying the correct target in the task\nagnostic inference. Finally, we demonstrate that popular existing continual\nlearning methods gain a performance boost when they adopt SAM as a starting\npoint.\n", "doi": null, "arxiv_id": "2101.12136", "pdf_urls": ["https://arxiv.org/pdf/2101.12136"], "github": ["https://github.com/GhadaSokar/Self-Attention-Meta-Learner-for-Continual-Learning"], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "20th International Conference on Autonomous Agents and Multiagent\n  Systems (AAMAS 2021)", "categories": "cs.LG cs.AI cs.CV"}
{"title": "Deceptive Reinforcement Learning for Privacy-Preserving Planning", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Zhengshang Liu", "Yue Yang", "Tim Miller", "and Peta Masters"], "abstract": "  In this paper, we study the problem of deceptive reinforcement learning to\npreserve the privacy of a reward function. Reinforcement learning is the\nproblem of finding a behaviour policy based on rewards received from\nexploratory behaviour. A key ingredient in reinforcement learning is a reward\nfunction, which determines how much reward (negative or positive) is given and\nwhen. However, in some situations, we may want to keep a reward function\nprivate; that is, to make it difficult for an observer to determine the reward\nfunction used. We define the problem of privacy-preserving reinforcement\nlearning, and present two models for solving it. These models are based on\ndissimulation -- a form of deception that `hides the truth'. We evaluate our\nmodels both computationally and via human behavioural experiments. Results show\nthat the resulting policies are indeed deceptive, and that participants can\ndetermine the true reward function less reliably than that of an honest agent.\n", "doi": null, "arxiv_id": "2102.03022", "pdf_urls": ["https://arxiv.org/pdf/2102.03022"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "Proceedings of the 20th International Conference on Autonomous\n  Agents and Multiagent Systems (AAMAS 2021)", "categories": "cs.LG cs.AI cs.MA"}
{"title": "Contrasting Centralized and Decentralized Critics in Multi-Agent\n  Reinforcement Learning", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Xueguang Lyu", "Yuchen Xiao", "Brett Daley", "Christopher Amato"], "abstract": "  Centralized Training for Decentralized Execution, where agents are trained\noffline using centralized information but execute in a decentralized manner\nonline, has gained popularity in the multi-agent reinforcement learning\ncommunity. In particular, actor-critic methods with a centralized critic and\ndecentralized actors are a common instance of this idea. However, the\nimplications of using a centralized critic in this context are not fully\ndiscussed and understood even though it is the standard choice of many\nalgorithms. We therefore formally analyze centralized and decentralized critic\napproaches, providing a deeper understanding of the implications of critic\nchoice. Because our theory makes unrealistic assumptions, we also empirically\ncompare the centralized and decentralized critic methods over a wide set of\nenvironments to validate our theories and to provide practical advice. We show\nthat there exist misconceptions regarding centralized critics in the current\nliterature and show that the centralized critic design is not strictly\nbeneficial, but rather both centralized and decentralized critics have\ndifferent pros and cons that should be taken into account by algorithm\ndesigners.\n", "doi": null, "arxiv_id": "2102.04402", "pdf_urls": ["https://arxiv.org/pdf/2102.04402"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "Proceedings of the 20th International Conference on Autonomous\n  Agents and MultiAgent Systems (AAMAS). 2021", "categories": "cs.LG cs.AI"}
{"title": "MAPFAST: A Deep Algorithm Selector for Multi Agent Path Finding using\n  Shortest Path Embeddings", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Jingyao Ren", "Vikraman Sathiyanarayanan", "Eric Ewing", "Baskin Senbaslar\n  and Nora Ayanian"], "abstract": "  Solving the Multi-Agent Path Finding (MAPF) problem optimally is known to be\nNP-Hard for both make-span and total arrival time minimization. While many\nalgorithms have been developed to solve MAPF problems, there is no dominating\noptimal MAPF algorithm that works well in all types of problems and no standard\nguidelines for when to use which algorithm. In this work, we develop the deep\nconvolutional network MAPFAST (Multi-Agent Path Finding Algorithm SelecTor),\nwhich takes a MAPF problem instance and attempts to select the fastest\nalgorithm to use from a portfolio of algorithms. We improve the performance of\nour model by including single-agent shortest paths in the instance embedding\ngiven to our model and by utilizing supplemental loss functions in addition to\na classification loss. We evaluate our model on a large and diverse dataset of\nMAPF instances, showing that it outperforms all individual algorithms in its\nportfolio as well as the state-of-the-art optimal MAPF algorithm selector. We\nalso provide an analysis of algorithm behavior in our dataset to gain a deeper\nunderstanding of optimal MAPF algorithms' strengths and weaknesses to help\nother researchers leverage different heuristics in algorithm designs.\n", "doi": null, "arxiv_id": "2102.12461", "pdf_urls": ["https://arxiv.org/pdf/2102.12461"], "github": ["https://github.com/refaev/robots_routing", "https://github.com/USC-ACTLab/MAPFAST"], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "AAMAS-2021, 1055-1062", "categories": "cs.MA cs.LG"}
{"title": "Sparse Training Theory for Scalable and Efficient Agents", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Decebal Constantin Mocanu", "Elena Mocanu", "Tiago Pinto", "Selima Curci", "Phuong H. Nguyen", "Madeleine Gibescu", "Damien Ernst", "Zita A. Vale"], "abstract": "  A fundamental task for artificial intelligence is learning. Deep Neural\nNetworks have proven to cope perfectly with all learning paradigms, i.e.\nsupervised, unsupervised, and reinforcement learning. Nevertheless, traditional\ndeep learning approaches make use of cloud computing facilities and do not\nscale well to autonomous agents with low computational resources. Even in the\ncloud, they suffer from computational and memory limitations, and they cannot\nbe used to model adequately large physical worlds for agents which assume\nnetworks with billions of neurons. These issues are addressed in the last few\nyears by the emerging topic of sparse training, which trains sparse networks\nfrom scratch. This paper discusses sparse training state-of-the-art, its\nchallenges and limitations while introducing a couple of new theoretical\nresearch directions which has the potential of alleviating sparse training\nlimitations to push deep learning scalability well beyond its current\nboundaries. Nevertheless, the theoretical advancements impact in complex\nmulti-agents settings is discussed from a real-world perspective, using the\nsmart grid case study.\n", "doi": null, "arxiv_id": "2103.01636", "pdf_urls": ["https://arxiv.org/pdf/2103.01636"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "20th International Conference on Autonomous Agents and Multiagent\n  Systems (AAMAS 2021)", "categories": "cs.AI cs.LG cs.MA cs.NE"}
{"title": "Minimum-Delay Adaptation in Non-Stationary Reinforcement Learning via\n  Online High-Confidence Change-Point Detection", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Lucas N. Alegre", "Ana L. C. Bazzan", "Bruno C. da Silva"], "abstract": "  Non-stationary environments are challenging for reinforcement learning\nalgorithms. If the state transition and/or reward functions change based on\nlatent factors, the agent is effectively tasked with optimizing a behavior that\nmaximizes performance over a possibly infinite random sequence of Markov\nDecision Processes (MDPs), each of which drawn from some unknown distribution.\nWe call each such MDP a context. Most related works make strong assumptions\nsuch as knowledge about the distribution over contexts, the existence of\npre-training phases, or a priori knowledge about the number, sequence, or\nboundaries between contexts. We introduce an algorithm that efficiently learns\npolicies in non-stationary environments. It analyzes a possibly infinite stream\nof data and computes, in real-time, high-confidence change-point detection\nstatistics that reflect whether novel, specialized policies need to be created\nand deployed to tackle novel contexts, or whether previously-optimized ones\nmight be reused. We show that (i) this algorithm minimizes the delay until\nunforeseen changes to a context are detected, thereby allowing for rapid\nresponses; and (ii) it bounds the rate of false alarm, which is important in\norder to minimize regret. Our method constructs a mixture model composed of a\n(possibly infinite) ensemble of probabilistic dynamics predictors that model\nthe different modes of the distribution over underlying latent MDPs. We\nevaluate our algorithm on high-dimensional continuous reinforcement learning\nproblems and show that it outperforms state-of-the-art (model-free and\nmodel-based) RL algorithms, as well as state-of-the-art meta-learning methods\nspecially designed to deal with non-stationarity.\n", "doi": null, "arxiv_id": "2105.09452", "pdf_urls": ["https://arxiv.org/pdf/2105.09452"], "github": ["https://github.com/LucasAlegre/mbcd"], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "Proceedings of the 20th International Conference on Autonomous\n  Agents and Multiagent Systems. 2021. 97-105", "categories": "cs.LG cs.AI"}
{"title": "A Hierarchical Bayesian model for Inverse RL in Partially-Controlled\n  Environments", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Kenneth Bogert (University of North Carolina Asheville) and Prashant\n  Doshi (University of Georgia)"], "abstract": "  Robots learning from observations in the real world using inverse\nreinforcement learning (IRL) may encounter objects or agents in the\nenvironment, other than the expert, that cause nuisance observations during the\ndemonstration. These confounding elements are typically removed in\nfully-controlled environments such as virtual simulations or lab settings. When\ncomplete removal is impossible the nuisance observations must be filtered out.\nHowever, identifying the source of observations when large amounts of\nobservations are made is difficult. To address this, we present a hierarchical\nBayesian model that incorporates both the expert's and the confounding\nelements' observations thereby explicitly modeling the diverse observations a\nrobot may receive. We extend an existing IRL algorithm originally designed to\nwork under partial occlusion of the expert to consider the diverse\nobservations. In a simulated robotic sorting domain containing both occlusion\nand confounding elements, we demonstrate the model's effectiveness. In\nparticular, our technique outperforms several other comparative methods, second\nonly to having perfect knowledge of the subject's trajectory.\n", "doi": null, "arxiv_id": "2107.05818", "pdf_urls": ["https://arxiv.org/pdf/2107.05818"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "Proceedings of the 21st International Conference on Autonomous\n  Agents and Multiagent Systems. 2022", "categories": "cs.LG cs.RO"}
{"title": "Recent Advances in Leveraging Human Guidance for Sequential\n  Decision-Making Tasks", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Ruohan Zhang", "Faraz Torabi", "Garrett Warnell", "Peter Stone"], "abstract": "  A longstanding goal of artificial intelligence is to create artificial agents\ncapable of learning to perform tasks that require sequential decision making.\nImportantly, while it is the artificial agent that learns and acts, it is still\nup to humans to specify the particular task to be performed. Classical\ntask-specification approaches typically involve humans providing stationary\nreward functions or explicit demonstrations of the desired tasks. However,\nthere has recently been a great deal of research energy invested in exploring\nalternative ways in which humans may guide learning agents that may, e.g., be\nmore suitable for certain tasks or require less human effort. This survey\nprovides a high-level overview of five recent machine learning frameworks that\nprimarily rely on human guidance apart from pre-specified reward functions or\nconventional, step-by-step action demonstrations. We review the motivation,\nassumptions, and implementation of each framework, and we discuss possible\nfuture research directions.\n", "doi": "10.1007/s10458-021-09514-w", "arxiv_id": "2107.05825", "pdf_urls": ["https://arxiv.org/pdf/2107.05825"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "JAAMAS 35 (2021) 1-39", "categories": "cs.AI cs.LG"}
{"title": "Empirical Game-Theoretic Analysis for Mean Field Games", "year": 2021, "venue_abbr": "AAMAS", "venue_name": "International Conference on Autonomous Agents and Multiagent Systems", "authors": ["Yongzhao Wang", "Michael P. Wellman"], "abstract": "  We present a simulation-based approach for solution of mean field games\n(MFGs), using the framework of empirical game-theoretical analysis (EGTA). Our\nprimary method employs a version of the double oracle, iteratively adding\nstrategies based on best response to the equilibrium of the empirical MFG among\nstrategies considered so far. We present Fictitious Play (FP) and Replicator\nDynamics as two subroutines for computing the empirical game equilibrium. Each\nsubroutine is implemented with a query-based method rather than maintaining an\nexplicit payoff matrix as in typical EGTA methods due to a representation issue\nwe highlight for MFGs. By introducing game model learning and regularization,\nwe significantly improve the sample efficiency of the primary method without\nsacrificing the overall learning performance. Theoretically, we prove that a\nNash equilibrium (NE) exists in the empirical MFG and show the convergence of\niterative EGTA to NE of the full MFG with either subroutine. We test the\nperformance of iterative EGTA in various games and show that it outperforms\ndirectly applying FP to MFGs in terms of iterations of strategy introduction.\n", "doi": null, "arxiv_id": "2112.00900", "pdf_urls": ["https://arxiv.org/pdf/2112.00900"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "conference", "journal_ref": "In Proc. of the 22nd International Conference on Autonomous Agents\n  and Multiagent Systems (AAMAS 2023)", "categories": "cs.MA"}