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ad91e86 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 | """Event-driven EvolvingNav controller (paper Equations 11–15, 20–22)."""
from __future__ import annotations
import math
from dataclasses import dataclass, field
from typing import Protocol
from evolvingnav_paper.filter import BeliefFilter, EvidenceLedger
from evolvingnav_paper.policy import candidate_utility
@dataclass(frozen=True)
class ViewEvidence:
evidence_id: str
state_id: int
surface_samples: frozenset[int]
detection_probability: float
covered_fraction: float = 1.0
pose_xyz: tuple[float, float, float] = (0.0, 0.0, 0.0)
@dataclass(frozen=True)
class AgentConfig:
max_inspections: int = 10
max_path_m: float = 100.0
chunk_m: float = 2.0
speed_mps: float = 1.0
inspection_s: float = 1.0
lambda_time: float = 0.05
lambda_inspect: float = 0.25
lambda_scan: float = 0.25
explore_cost: float = 2.0
discovery_probability: float = 0.5
speed_ema_alpha: float = 0.8
unknown_state: int | None = None
@dataclass
class AgentResult:
found: bool = False
actions: list[str] = field(default_factory=list)
inspections: list[int] = field(default_factory=list)
path_m: float = 0.0
elapsed_s: float = 0.0
posterior: dict[int, float] = field(default_factory=dict)
termination: str = ""
evidence_trace: list[dict] = field(default_factory=list)
class World(Protocol):
def distance(self, goal) -> float: ...
def move_chunk(self, goal, max_distance: float) -> tuple[float, float]: ...
def inspect(self, state: int) -> tuple[bool, list[ViewEvidence]]: ...
def explore(self, budget_m: float) -> tuple[dict[int, tuple[object, float]], float, float]: ...
class Agent:
def __init__(self, prior: dict[int, float], goals: dict[int, object], world: World,
transition, config: AgentConfig | None = None,
sample_count: dict[int, int] | None = None, controller=None,
memory=None, target_id: str | None = None,
time_origin_s: float = 0.0) -> None:
self.config = config or AgentConfig()
self.filter = BeliefFilter(prior, transition)
self.goals = {state: goal for state, goal in goals.items() if state in prior}
self.world = world
self.ledger = EvidenceLedger(sample_count=sample_count)
self.speed = self.config.speed_mps
self.controller = controller
self.memory = memory
self.target_id = target_id
self.time_origin_s = time_origin_s
def _return_probability(self, state: int, eta: float) -> float:
if state not in self.filter.posterior:
return 0.0
forecast = self.filter.arrival(eta)
# Equation 21 excludes probability already at this state.
return max(0.0, forecast.get(state, 0.0) - self.filter.posterior.get(state, 0.0)
* self.filter.transition.matrix(list(self.filter.posterior), eta)[
list(self.filter.posterior).index(state), list(self.filter.posterior).index(state)])
def _choose(self, result: AgentResult) -> int | str | None:
scored: list[tuple[float, int | str]] = []
for state, goal in self.goals.items():
distance = self.world.distance(goal)
if not math.isfinite(distance) or result.path_m + distance > self.config.max_path_m:
continue
eta = distance / self.speed + self.config.inspection_s
arrival = self.filter.arrival(eta)
return_probability = self._return_probability(state, eta)
if not self.ledger.eligible(
state, belief=self.filter.posterior.get(state, 0.0),
return_probability=return_probability, new_coverage=0.0,
dynamic=self.filter.transition.dynamic,
now_s=result.elapsed_s,
):
continue
uncovered = max(0.0, 1.0 - self.ledger.coverage(state))
new_detection = (
self.world.expected_new_detection(state, uncovered)
if hasattr(self.world, "expected_new_detection") else uncovered
)
utility = candidate_utility(
arrival_probability=arrival.get(state, 0.0),
new_detection_probability=new_detection,
distance_m=distance, eta_s=eta,
lambda_time=self.config.lambda_time,
lambda_inspect=self.config.lambda_inspect,
)
scored.append((utility, state))
unknown = self.config.unknown_state
if unknown is not None and self.filter.posterior.get(unknown, 0.0) > 0:
utility = (self.filter.posterior[unknown] * self.config.discovery_probability
/ (self.config.explore_cost + self.config.lambda_scan))
scored.append((utility, "EXPLORE"))
if not scored:
return None
preferred = max(scored, key=lambda row: (row[0], -row[1] if isinstance(row[1], int) else 0))
if self.controller is not None:
labels = [f"NAVIGATE_TO({action})" if isinstance(action, int) else action
for _, action in scored]
chosen = self.controller.choose(labels, {
"belief": self.filter.posterior,
"utilities": dict(zip(labels, [utility for utility, _ in scored], strict=True)),
"elapsed_s": result.elapsed_s, "path_m": result.path_m,
})
for utility, action in scored:
label = f"NAVIGATE_TO({action})" if isinstance(action, int) else action
if label == chosen and utility >= preferred[0] - 1e-9:
return action
return preferred[1]
def _admit_negative(self, evidence: list[ViewEvidence], now_s: float,
result: AgentResult) -> None:
for item in evidence:
new_coverage = self.ledger.admit(item)
if new_coverage > 0:
probability = min(1.0, item.detection_probability * new_coverage
/ max(item.covered_fraction, 1e-9))
before = self.filter.posterior.get(item.state_id, 0.0)
applied = self.filter.negative(
{item.state_id: probability},
f"{item.evidence_id}:{item.state_id}",
)
if applied:
result.evidence_trace.append({
"evidence_id": item.evidence_id,
"state_id": item.state_id,
"time_s": now_s,
"new_coverage": new_coverage,
"detection_probability": probability,
"prior": before,
"posterior": self.filter.posterior.get(item.state_id, 0.0),
})
if applied and self.memory is not None:
self.memory.record_negative(
item.evidence_id, item.state_id,
self.time_origin_s + now_s, item.pose_xyz
)
def run(self) -> AgentResult:
result = AgentResult()
for _ in range(self.config.max_inspections + len(self.goals) * 20):
selected = self._choose(result)
if selected is None:
result.termination = "search_exhausted"
unknown = self.config.unknown_state
unknown_mass = self.filter.posterior.get(unknown, 0.0) if unknown is not None else 0.0
searchable = sum(self.filter.posterior.get(state, 0.0) for state in self.goals)
if unknown_mass < 0.05 and searchable < 0.05:
result.actions.append("NOT_FOUND")
break
if selected == "EXPLORE":
result.actions.append("EXPLORE")
discovered, duration, displacement = self.world.explore(
self.config.max_path_m - result.path_m
)
self.filter.advance(duration)
result.elapsed_s += duration
result.path_m += displacement
if not discovered:
result.termination = "exploration_exhausted"
break
unknown = self.config.unknown_state
mass = self.filter.posterior[unknown]
total_weight = sum(weight for _, weight in discovered.values())
allocated = min(mass, total_weight)
self.filter.posterior[unknown] -= allocated
for state, (goal, probability) in discovered.items():
self.goals[state] = goal
if hasattr(self.world, "sample_count"):
self.ledger.sample_count[state] = self.world.sample_count(state)
if hasattr(self.filter.transition, "add_candidate"):
self.filter.transition.add_candidate(state)
self.filter.posterior[state] = self.filter.posterior.get(state, 0.0) + (
allocated * probability / total_weight)
continue
state = int(selected)
goal = self.goals[state]
result.actions.append(f"NAVIGATE_TO({state})")
while self.world.distance(goal) > 1e-4:
remaining = self.config.max_path_m - result.path_m
if remaining <= 0:
result.termination = "path_budget_exhausted"
result.posterior = self.filter.posterior.copy()
return result
displacement, duration = self.world.move_chunk(goal, min(self.config.chunk_m, remaining))
if displacement <= 0 or duration <= 0:
result.termination = "navigation_blocked"
result.posterior = self.filter.posterior.copy()
return result
self.filter.advance(duration)
result.path_m += displacement
result.elapsed_s += duration
self.speed = (self.config.speed_ema_alpha * self.speed
+ (1 - self.config.speed_ema_alpha) * displacement / duration)
if hasattr(self.world, "observe_chunk"):
self._admit_negative(self.world.observe_chunk(), result.elapsed_s, result)
if self._choose(result) != state:
break
else:
result.actions.append(f"INSPECT({state})")
if hasattr(self.world, "advance_time"):
self.world.advance_time(self.config.inspection_s)
self.filter.advance(self.config.inspection_s)
result.elapsed_s += self.config.inspection_s
detected, evidence = self.world.inspect(state)
result.inspections.append(state)
self.ledger.mark_inspected(state, now_s=result.elapsed_s)
if detected:
detection = getattr(self.world, "last_detection", None)
if (self.memory is not None and self.target_id is not None
and detection is not None):
self.memory.observe(
self.target_id, state, self.time_origin_s + result.elapsed_s,
detection["confidence"], detection["evidence_id"],
detection["world_point"],
)
result.found = True
result.actions.append("STOP")
break
self._admit_negative(evidence, result.elapsed_s, result)
if len(result.inspections) >= self.config.max_inspections:
result.termination = "inspection_budget_exhausted"
break
result.posterior = self.filter.posterior.copy()
return result
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