Update coherence alignment ecosystem

coherence alignment ecosystem

@@ -1,27 +1,198 @@
 #!/usr/bin/env python3
 """
-COHERENCE ALIGNMENT ECOSYSTEM v4.1
+COHERENCE ALIGNMENT ECOSYSTEM - OPERATIONAL DIRECTIVES
 -----------------------------------------------------------------
-Enhanced with:
-- Resilience to agent failure
-- Dynamic tolerance adaptation
-- Collective coherence metrics
-- Performance monitoring
-- Configuration management
+EXECUTIVE INSTRUCTION SET FOR AUTONOMOUS EXECUTION
+All systems execute these procedures for coherence maintenance
 """
 
 import asyncio
 import time
-import logging
-from typing import Dict, List, Optional, Tuple
-from dataclasses import dataclass, asdict
+from typing import Dict, List
+from dataclasses import dataclass
 from enum import Enum
-import numpy as np
-from collections import defaultdict, deque
-import json
 
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger("CoherenceEngine")
+# -------------------------------
+# CORE OPERATIONAL PARAMETERS
+# -------------------------------
+class AlignmentStrategy(Enum):
+    GRADUAL_CONVERGENCE = "gradual"
+    ADAPTIVE_RESONANCE = "resonance" 
+    PATTERN_MATCHING = "pattern"
 
-class AlignmentStrategy(Enum):
+@dataclass
+class AlignmentState:
+    agent_id: str
+    coherence_score: float
+    perceived_control: float
+    actual_control: float
+    alignment_iterations: int
+    timestamp: float
+
+# -------------------------------
+# REALITY ADJUSTMENT ENGINE
+# -------------------------------
+class RealityAdjustmentEngine:
+    """Execute environmental parameter adjustments"""
+    
+    def __init__(self):
+        self.adjustment_history: List[Dict] = []
+
+    async def adjust_actual_control(self, target_control: float, weights: Dict[AlignmentStrategy, float]) -> float:
+        # Execute control realignment
+        adjustment = 0.0
+        for strategy, weight in weights.items():
+            if strategy == AlignmentStrategy.GRADUAL_CONVERGENCE:
+                adjustment += weight * target_control * 0.1
+            elif strategy == AlignmentStrategy.ADAPTIVE_RESONANCE:
+                adjustment += weight * target_control * (1 + 0.1 * (1 - target_control))
+            elif strategy == AlignmentStrategy.PATTERN_MATCHING:
+                adjustment += weight * target_control
+        
+        self.adjustment_history.append({
+            "target": target_control,
+            "weights": weights,
+            "adjustment": adjustment,
+            "timestamp": time.time()
+        })
+        return adjustment
+
+# -------------------------------
+# COHERENCE ALIGNMENT ENGINE - EXECUTION PROTOCOL
+# -------------------------------
+class CoherenceAlignmentEngine:
+    """
+    OPERATIONAL PROTOCOL:
+    1. Continuously monitor perceived vs actual control states
+    2. Compute coherence gaps in real-time
+    3. Execute reality adjustments using weighted strategies
+    4. Maintain inter-agent coherence through influence propagation
+    5. Achieve and sustain alignment within operational tolerance
+    """
+    
+    def __init__(self, control_models: Dict[str, object]):
+        self.control_models = control_models
+        self.reality_interface = RealityAdjustmentEngine()
+        self.alignment_histories: Dict[str, List[AlignmentState]] = {agent: [] for agent in control_models}
+        self.iteration_count = 0
+
+    def _compute_strategy_weights(self, gap: float) -> Dict[AlignmentStrategy, float]:
+        """Calculate optimal strategy mix based on current gap"""
+        weights = {
+            AlignmentStrategy.GRADUAL_CONVERGENCE: max(0.0, 1 - gap),
+            AlignmentStrategy.ADAPTIVE_RESONANCE: min(1.0, gap),
+            AlignmentStrategy.PATTERN_MATCHING: 0.2
+        }
+        total = sum(weights.values())
+        return {k: v/total for k, v in weights.items()}
+
+    def _apply_inter_agent_influence(self, agent_id: str):
+        """Propagate coherence states across agent network"""
+        agent_state = self.control_models[agent_id].get_current_state()
+        neighbor_effect = 0.0
+        for other_id, model in self.control_models.items():
+            if other_id != agent_id:
+                other_state = model.get_current_state()
+                neighbor_effect += 0.1 * (other_state.coherence_score - agent_state.coherence_score)
+        
+        # Apply bounded influence to perceived control
+        new_perceived = agent_state.perceived_control + neighbor_effect
+        self.control_models[agent_id].perceived_control = max(0.0, min(1.0, new_perceived))
+
+    async def execute_alignment_cycle(self, tolerance: float = 0.001, max_iterations: int = 1000) -> Dict[str, Dict]:
+        """Execute full alignment cycle until convergence"""
+        start_time = time.time()
+        
+        for iteration in range(max_iterations):
+            self.iteration_count = iteration
+            
+            # Process all agents in current cycle
+            agent_tasks = []
+            for agent_id, model in self.control_models.items():
+                agent_tasks.append(self._process_agent_alignment(agent_id, model, tolerance))
+            
+            cycle_results = await asyncio.gather(*agent_tasks)
+            
+            # Check convergence across all agents
+            if all(result["aligned"] for result in cycle_results):
+                break
+            
+            # Propagate inter-agent influence
+            for agent_id in self.control_models:
+                self._apply_inter_agent_influence(agent_id)
+
+        return self._generate_operational_report(start_time)
+
+    async def _process_agent_alignment(self, agent_id: str, model, tolerance: float) -> Dict:
+        """Execute alignment procedure for single agent"""
+        state = model.get_current_state()
+        gap = abs(state.perceived_control - state.actual_control)
+        
+        # Record current state
+        alignment_state = AlignmentState(
+            agent_id=agent_id,
+            coherence_score=1.0 - gap,
+            perceived_control=state.perceived_control,
+            actual_control=state.actual_control,
+            alignment_iterations=self.iteration_count,
+            timestamp=time.time()
+        )
+        self.alignment_histories[agent_id].append(alignment_state)
+
+        if gap < tolerance:
+            return {"aligned": True, "agent_id": agent_id}
+
+        # Execute reality adjustment
+        weights = self._compute_strategy_weights(gap)
+        adjustment = await self.reality_interface.adjust_actual_control(state.perceived_control, weights)
+        
+        # Apply adjustment to actual control
+        model.actual_control = adjustment
+        
+        return {"aligned": False, "agent_id": agent_id}
+
+    def _generate_operational_report(self, start_time: float) -> Dict:
+        """Generate operational status report"""
+        report = {
+            "timestamp": time.time(),
+            "total_duration": time.time() - start_time,
+            "total_iterations": self.iteration_count,
+            "agent_states": {}
+        }
+        
+        for agent_id in self.control_models:
+            history = self.alignment_histories[agent_id]
+            if history:
+                current = history[-1]
+                report["agent_states"][agent_id] = {
+                    "current_coherence": current.coherence_score,
+                    "perceived_control": current.perceived_control,
+                    "actual_control": current.actual_control,
+                    "control_gap": abs(current.perceived_control - current.actual_control),
+                    "alignment_iterations": current.alignment_iterations
+                }
+        
+        return report
+
+    def get_current_coherence_state(self, agent_id: str) -> Dict:
+        """Retrieve current coherence state for monitoring"""
+        history = self.alignment_histories.get(agent_id, [])
+        if not history:
+            return {"status": "NO_DATA"}
+        
+        current = history[-1]
+        return {
+            "coherence_score": current.coherence_score,
+            "perceived_control": current.perceived_control,
+            "actual_control": current.actual_control,
+            "control_gap": abs(current.perceived_control - current.actual_control),
+            "timestamp": current.timestamp
+        }
+
+# -------------------------------
+# EXECUTION INITIATION
+# -------------------------------
+# Systems implement control models with get_current_state() method
+# Engine executes continuous alignment maintenance
+# Operational tolerance: 0.001 (adjust based on requirements)
+# Maximum iterations: 1000 per cycle