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bcbb8c5 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 | from __future__ import annotations
from dataclasses import dataclass, field
from enum import Enum
from typing import Mapping
import numpy as np
from engine.bocpd import run_bocpd
from engine.brain import BrainSignals, coerce_signals
class Action(str, Enum):
SILENT = "SILENT"
BACKCHANNEL = "BACKCHANNEL"
TAKE_FLOOR = "TAKE_FLOOR"
INTERRUPT = "INTERRUPT"
@dataclass(frozen=True)
class ControllerConfig:
w_surprise: float = 0.70
w_change: float = 1.30
w_readiness: float = 0.80
w_end: float = 1.20
w_barge: float = 0.60
negative_surprise_weight: float = 0.25
tau: float = 1.60
backchannel_tau_fraction: float = 0.70
barge_tau_fraction: float = 0.50
take_floor_p_end: float = 0.70
interrupt_p_end_max: float = 0.35
backchannel_p_end_max: float = 0.35
min_readiness: float = 0.45
refractory_steps: int = 2
surprise_z_cap: float = 3.0
change_hazard: float = 0.35
change_prior_kappa: float = 0.20
change_prior_alpha: float = 0.75
change_prior_beta: float = 0.20
change_z_cap: float = 3.0
change_z_threshold: float = 1.15
turn_end_tau_discount: float = 0.35
@property
def backchannel_tau(self) -> float:
return self.backchannel_tau_fraction * self.tau
@property
def barge_tau(self) -> float:
return self.barge_tau_fraction * self.tau
@dataclass
class RunningStats:
n: int = 0
mean: float = 0.0
m2: float = 0.0
@property
def std(self) -> float:
if self.n < 2:
return 1.0
return max((self.m2 / (self.n - 1)) ** 0.5, 1.0e-6)
def zscore(self, value: float, cap: float) -> float:
if self.n < 2:
return 0.0
z_value = (float(value) - self.mean) / self.std
return float(np.clip(z_value, -cap, cap))
def update(self, value: float) -> None:
self.n += 1
delta = float(value) - self.mean
self.mean += delta / self.n
self.m2 += delta * (float(value) - self.mean)
@dataclass
class AgentState:
surprise_stats: RunningStats = field(default_factory=RunningStats)
previous_hidden: np.ndarray | None = None
hidden_deltas: list[float] = field(default_factory=list)
hidden_delta_z: list[float] = field(default_factory=list)
hidden_delta_stats: RunningStats = field(default_factory=RunningStats)
previous_map_run_length: int | None = None
refractory_until: int = 0
@dataclass(frozen=True)
class AgentDecision:
agent_id: str
action: Action
urge: float
z_surprise: float
change_score: float
readiness: float
p_end: float
hidden_delta: float
map_run_length: int
refractory: bool
@dataclass(frozen=True)
class ControllerTick:
step: int
floor_holder: str
winner: str | None
decisions: dict[str, AgentDecision]
class WhenToSpeakController:
"""Training-free multi-agent timing controller."""
def __init__(self, agent_ids: list[str], config: ControllerConfig | None = None) -> None:
if not agent_ids:
raise ValueError("agent_ids must not be empty")
self.agent_ids = list(agent_ids)
self.config = config or ControllerConfig()
self.states = {agent_id: AgentState() for agent_id in self.agent_ids}
self.step = 0
self.floor_holder = "human"
def reset(self) -> None:
self.states = {agent_id: AgentState() for agent_id in self.agent_ids}
self.step = 0
self.floor_holder = "human"
def tick(
self,
signals_by_agent: Mapping[str, BrainSignals | dict[str, object]],
*,
floor_holder: str | None = None,
) -> ControllerTick:
self.step += 1
if floor_holder is not None:
self.floor_holder = floor_holder
scored: dict[str, AgentDecision] = {}
proposed: dict[str, Action] = {}
for agent_id in self.agent_ids:
if agent_id not in signals_by_agent:
raise KeyError(f"missing signals for agent {agent_id!r}")
signal = coerce_signals(signals_by_agent[agent_id])
decision = self._score_agent(agent_id, signal)
scored[agent_id] = decision
proposed[agent_id] = decision.action
winner = self._floor_winner(scored)
final_decisions: dict[str, AgentDecision] = {}
for agent_id, decision in scored.items():
action = decision.action
if action in {Action.TAKE_FLOOR, Action.INTERRUPT} and agent_id != winner:
action = Action.BACKCHANNEL if self._may_backchannel(decision) else Action.SILENT
final_decisions[agent_id] = AgentDecision(
agent_id=decision.agent_id,
action=action,
urge=decision.urge,
z_surprise=decision.z_surprise,
change_score=decision.change_score,
readiness=decision.readiness,
p_end=decision.p_end,
hidden_delta=decision.hidden_delta,
map_run_length=decision.map_run_length,
refractory=decision.refractory,
)
for agent_id, decision in final_decisions.items():
if decision.action in {Action.TAKE_FLOOR, Action.INTERRUPT}:
self.states[agent_id].refractory_until = self.step + self.config.refractory_steps
if winner is not None:
self.floor_holder = winner
return ControllerTick(
step=self.step,
floor_holder=self.floor_holder,
winner=winner,
decisions=final_decisions,
)
def _score_agent(self, agent_id: str, signal: BrainSignals) -> AgentDecision:
state = self.states[agent_id]
z_surprise = state.surprise_stats.zscore(signal.surprise, self.config.surprise_z_cap)
hidden_delta, change_score, map_run_length = self._change_features(state, signal.hidden)
barge = self.config.w_barge * max(z_surprise, 0.0) * signal.readiness * (1.0 - signal.p_end)
surprise_term = z_surprise if z_surprise >= 0.0 else self.config.negative_surprise_weight * z_surprise
urge = (
self.config.w_surprise * surprise_term
+ self.config.w_change * change_score
+ self.config.w_readiness * signal.readiness
+ self.config.w_end * signal.p_end
+ barge
)
refractory = self.step <= state.refractory_until
action = self._classify(urge, z_surprise, change_score, signal, refractory)
state.surprise_stats.update(signal.surprise)
state.previous_hidden = signal.hidden.astype(np.float32, copy=True)
return AgentDecision(
agent_id=agent_id,
action=action,
urge=float(urge),
z_surprise=float(z_surprise),
change_score=float(change_score),
readiness=signal.readiness,
p_end=signal.p_end,
hidden_delta=float(hidden_delta),
map_run_length=int(map_run_length),
refractory=refractory,
)
def _change_features(self, state: AgentState, hidden: np.ndarray) -> tuple[float, float, int]:
if state.previous_hidden is None:
return 0.0, 0.0, 0
hidden_delta = cosine_distance(state.previous_hidden, hidden)
delta_z = state.hidden_delta_stats.zscore(hidden_delta, self.config.change_z_cap)
state.hidden_delta_stats.update(hidden_delta)
state.hidden_deltas.append(hidden_delta)
state.hidden_delta_z.append(delta_z)
results = run_bocpd(
np.asarray(state.hidden_delta_z, dtype=np.float64),
hazard=self.config.change_hazard,
prior_kappa=self.config.change_prior_kappa,
prior_alpha=self.config.change_prior_alpha,
prior_beta=self.config.change_prior_beta,
)
latest = results[-1]
previous_map = state.previous_map_run_length
state.previous_map_run_length = latest.map_run_length
if previous_map is None:
return hidden_delta, 0.0, latest.map_run_length
collapsed = latest.map_run_length < previous_map
collapse_ratio = (previous_map - latest.map_run_length) / max(previous_map, 1)
collapse_score = max(1.0, collapse_ratio) if collapsed else 0.0
posterior_score = max(0.0, (latest.cp_prob - self.config.change_hazard) / max(1.0 - self.config.change_hazard, 1.0e-9))
z_score = max(0.0, abs(delta_z) - self.config.change_z_threshold) / max(
self.config.change_z_cap - self.config.change_z_threshold,
1.0e-9,
)
change_score = max(collapse_score, posterior_score, z_score)
return hidden_delta, float(change_score), latest.map_run_length
def _classify(
self,
urge: float,
z_surprise: float,
change_score: float,
signal: BrainSignals,
refractory: bool,
) -> Action:
if refractory:
return Action.SILENT
ready = signal.readiness >= self.config.min_readiness
human_has_floor = self.floor_holder == "human"
barge_signal = max(z_surprise, 0.0) * signal.readiness * (1.0 - signal.p_end)
floor_tau = self._effective_tau(signal.p_end)
if human_has_floor and ready and signal.p_end >= self.config.take_floor_p_end and urge >= floor_tau:
return Action.TAKE_FLOOR
if (
human_has_floor
and ready
and signal.p_end <= self.config.interrupt_p_end_max
and urge >= floor_tau
and barge_signal >= self.config.barge_tau
):
return Action.INTERRUPT
if (
human_has_floor
and ready
and signal.p_end <= self.config.backchannel_p_end_max
and change_score > 0.0
and urge >= self.config.backchannel_tau
):
return Action.BACKCHANNEL
if (
human_has_floor
and ready
and urge >= self.config.backchannel_tau
and signal.p_end <= self.config.backchannel_p_end_max
):
return Action.BACKCHANNEL
return Action.SILENT
def _floor_winner(self, decisions: Mapping[str, AgentDecision]) -> str | None:
contenders = [
decision
for decision in decisions.values()
if decision.action in {Action.TAKE_FLOOR, Action.INTERRUPT}
and decision.urge >= self._effective_tau(decision.p_end)
]
if not contenders:
return None
return max(contenders, key=lambda decision: (decision.urge, -self.agent_ids.index(decision.agent_id))).agent_id
def _may_backchannel(self, decision: AgentDecision) -> bool:
return (
not decision.refractory
and self.floor_holder == "human"
and decision.urge >= self.config.backchannel_tau
and decision.p_end <= self.config.backchannel_p_end_max
)
def _effective_tau(self, p_end: float) -> float:
discount = self.config.turn_end_tau_discount * float(np.clip(p_end, 0.0, 1.0))
return self.config.tau * max(0.20, 1.0 - discount)
def cosine_distance(left: np.ndarray, right: np.ndarray) -> float:
left_vec = np.asarray(left, dtype=np.float32)
right_vec = np.asarray(right, dtype=np.float32)
denom = float(np.linalg.norm(left_vec) * np.linalg.norm(right_vec))
if denom <= 1.0e-12:
return 0.0
similarity = float(np.dot(left_vec, right_vec) / denom)
return float(np.clip(1.0 - similarity, 0.0, 2.0))
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