unified separability writing

engine/lib/coi.py

@@ -1,12 +1,15 @@
 import numpy as np
 from typing import Dict
 
+from lib.agent_probability import DEFAULT_AGENT_PRIOR, estimate_agent_probability
+
 
 def compute_agent_probability(
     trajectory: list,
     human_transitions: Dict,
     agent_transitions: Dict,
     temperature: float = 1.0,
+    prior_agent: float = DEFAULT_AGENT_PRIOR,
 ) -> float:
     """estimate agent probability via KL divergence between trajectory transitions and reference models
 
@@ -18,10 +21,10 @@ def compute_agent_probability(
         agent_transitions: reference transition dict from agent MDP (event->event->prob)
 
     returns:
-        agent probability in [0, 1] via softmax over KL divergences
+        agent probability in [0, 1] via sigma((delta_h - delta_a) / T)
     """
     if len(trajectory) < 2:
-        return 0.0  # insufficient data, assume human
+        return float(prior_agent)
 
     # build empirical transition distribution from trajectory
     trans_counts = {}
@@ -54,11 +57,12 @@ def compute_agent_probability(
     kl_human = kl_div(empirical, human_transitions)
     kl_agent = kl_div(empirical, agent_transitions)
 
-    # convert to probability via softmax (lower KL = higher prob)
-    t = float(max(temperature, 1e-6))
-    exp_h = np.exp(-kl_human / t)
-    exp_a = np.exp(-kl_agent / t)
-    return float(exp_a / (exp_h + exp_a + 1e-10))
+    return estimate_agent_probability(
+        delta_h=kl_human,
+        delta_a=kl_agent,
+        temperature=temperature,
+        prior_agent=prior_agent,
+    )
 
 
 def extract_purchases(trajectories: list) -> Dict[int, int]: