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31226fd | 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 | """Operational, non-psychological behavior profile for controlled tasks."""
from __future__ import annotations
import hashlib
import json
import os
import tempfile
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any
from src.embodiment.models import ActionCommand
BEHAVIOR_SCHEMA_VERSION = 1
_DIMENSIONS = (
"exploration",
"novelty_response",
"persistence",
"strategy_switching",
"cost_weight",
)
def _digest(value: dict[str, Any]) -> str:
unsigned = json.loads(json.dumps(value, ensure_ascii=True))
unsigned.pop("integrity_digest", None)
return hashlib.sha256(
json.dumps(
unsigned, sort_keys=True, separators=(",", ":"), ensure_ascii=True
).encode("utf-8")
).hexdigest()
def _bounded(value: float) -> float:
return max(0.0, min(1.0, float(value)))
@dataclass(slots=True)
class BehaviorProfile:
"""Three-level profile with deterministic, slowly changing disposition."""
profile_id: str
initial: dict[str, float] = field(default_factory=dict)
situational: dict[str, float] = field(default_factory=dict)
disposition: dict[str, float] = field(default_factory=dict)
update_rate: float = 0.05
update_log: list[dict[str, Any]] = field(default_factory=list[dict[str, Any]])
max_update_log: int = 256
schema_version: int = BEHAVIOR_SCHEMA_VERSION
def __post_init__(self) -> None:
defaults = {
"exploration": 0.2,
"novelty_response": 0.5,
"persistence": 0.5,
"strategy_switching": 0.3,
"cost_weight": 0.5,
}
for dimension in _DIMENSIONS:
self.initial[dimension] = _bounded(
self.initial.get(dimension, defaults[dimension])
)
self.situational[dimension] = _bounded(self.situational.get(dimension, 0.0))
self.disposition[dimension] = _bounded(
self.disposition.get(dimension, self.initial[dimension])
)
if not 0.0 < self.update_rate <= 1.0:
raise ValueError("update_rate must be between 0 and 1")
if self.max_update_log <= 0:
raise ValueError("max_update_log must be positive")
self.update_log = self.update_log[-self.max_update_log :]
def value(self, dimension: str) -> float:
if dimension not in _DIMENSIONS:
raise KeyError(dimension)
return _bounded(self.disposition[dimension] + self.situational[dimension])
def select_action(
self, candidates: tuple[ActionCommand, ...], *, tick: int
) -> ActionCommand | None:
"""Select a candidate using profile state and simulation tick only."""
if not candidates:
return None
if len(candidates) == 1:
return candidates[0]
exploration = self.value("exploration")
bucket = (tick * 1103515245 + 12345) % 1000
if bucket < int(exploration * 1000):
index = tick % len(candidates)
return candidates[index]
return candidates[0]
def update(self, *, success: bool, tick: int, source: str = "task_outcome") -> None:
"""Apply the disclosed bounded update rule using simulation time."""
target = 0.0 if success else 1.0
old = dict(self.disposition)
for dimension in _DIMENSIONS:
if dimension == "persistence":
target = 0.0 if success else 1.0
elif dimension == "strategy_switching":
target = 1.0 if not success else 0.0
else:
target = self.initial[dimension]
self.disposition[dimension] = _bounded(
self.disposition[dimension]
+ self.update_rate * (target - self.disposition[dimension])
)
self.situational["persistence"] = _bounded(0.2 if success else 0.6)
self.situational["strategy_switching"] = _bounded(0.2 if not success else 0.0)
self.update_log.append(
{
"tick": tick,
"source": source,
"rule": "bounded_ema_to_outcome_target_v1",
"success": success,
"before": old,
"after": dict(self.disposition),
}
)
self.update_log = self.update_log[-self.max_update_log :]
def state_dict(self) -> dict[str, Any]:
state: dict[str, Any] = {
"schema_version": self.schema_version,
"owner": "profiles.behavior",
"profile_id": self.profile_id,
"initial": dict(self.initial),
"situational": dict(self.situational),
"disposition": dict(self.disposition),
"update_rate": self.update_rate,
"update_log": list(self.update_log),
"max_update_log": self.max_update_log,
}
state["integrity_digest"] = _digest(state)
return state
def save(self, path: Path) -> Path:
payload = json.dumps(
self.state_dict(), sort_keys=True, separators=(",", ":"), ensure_ascii=True
).encode("utf-8")
path.parent.mkdir(parents=True, exist_ok=True)
fd, temporary = tempfile.mkstemp(prefix=f".{path.name}.", dir=str(path.parent))
try:
with os.fdopen(fd, "wb") as stream:
stream.write(payload)
stream.flush()
os.fsync(stream.fileno())
os.replace(temporary, path)
finally:
if os.path.exists(temporary):
os.unlink(temporary)
return path
@classmethod
def load(cls, path: Path) -> "BehaviorProfile":
state = json.loads(path.read_text(encoding="utf-8"))
if (
not isinstance(state, dict)
or state.get("schema_version") != BEHAVIOR_SCHEMA_VERSION
):
raise ValueError("unsupported behavior profile schema")
if state.get("owner") != "profiles.behavior" or state.get(
"integrity_digest"
) != _digest(state):
raise ValueError("behavior profile integrity check failed")
return cls(
str(state["profile_id"]),
dict(state["initial"]),
dict(state["situational"]),
dict(state["disposition"]),
float(state["update_rate"]),
list(state["update_log"]),
int(state.get("max_update_log", 256)),
)
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