#!/usr/bin/env python3 """ COHERENCE ALIGNMENT ECOSYSTEM - OPERATIONAL DIRECTIVES WITH EARLY CONVERGENCE ----------------------------------------------------------------- EXECUTIVE INSTRUCTION SET FOR AUTONOMOUS EXECUTION All systems execute these procedures for coherence maintenance """ import asyncio import time import numpy as np from typing import Dict, List, Tuple from dataclasses import dataclass from enum import Enum # ------------------------------- # CORE OPERATIONAL PARAMETERS # ------------------------------- class AlignmentStrategy(Enum): GRADUAL_CONVERGENCE = "gradual" ADAPTIVE_RESONANCE = "resonance" PATTERN_MATCHING = "pattern" @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 WITH EARLY CONVERGENCE HEURISTICS: 1. Monitor convergence velocity and acceleration 2. Detect oscillation patterns for early termination 3. Predict convergence points using trend analysis 4. Apply adaptive tolerance based on system stability 5. Execute minimal necessary adjustments """ 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 self.convergence_cache: Dict[str, Dict] = {} 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)) def _detect_early_convergence(self, agent_id: str, current_gap: float, tolerance: float) -> Tuple[bool, float]: """ EARLY CONVERGENCE HEURISTICS: - Convergence velocity analysis - Oscillation pattern detection - Trend-based convergence prediction - Adaptive tolerance adjustment """ history = self.alignment_histories[agent_id] if len(history) < 3: return False, tolerance # Calculate convergence metrics gaps = [abs(h.perceived_control - h.actual_control) for h in history[-5:]] # Heuristic 1: Convergence velocity if len(gaps) >= 2: velocity = gaps[-2] - gaps[-1] # Positive = converging if velocity > 0 and current_gap < tolerance * 3: # Accelerating convergence near target return True, tolerance # Heuristic 2: Oscillation detection if len(gaps) >= 4: oscillations = sum(1 for i in range(1, len(gaps)) if (gaps[i] - gaps[i-1]) * (gaps[i-1] - gaps[i-2]) < 0) if oscillations >= 2 and current_gap < tolerance * 2: # System oscillating within acceptable range return True, tolerance * 1.5 # Heuristic 3: Linear convergence prediction if len(gaps) >= 3: try: x = np.arange(len(gaps)) slope, intercept = np.polyfit(x, gaps, 1) predicted_zero = -intercept / slope if slope != 0 else float('inf') if 0 < predicted_zero - len(gaps) < 2 and current_gap < tolerance * 2: # Linear prediction shows imminent convergence return True, tolerance except: pass # Heuristic 4: Adaptive tolerance for stable systems if len(gaps) >= 5: gap_std = np.std(gaps) if gap_std < tolerance * 0.5: # System is stable with low variance effective_tolerance = max(tolerance, gap_std * 2) return current_gap < effective_tolerance, effective_tolerance return False, tolerance def _calculate_convergence_confidence(self, agent_id: str) -> float: """Calculate confidence score in convergence stability""" history = self.alignment_histories[agent_id] if len(history) < 2: return 0.0 gaps = [abs(h.perceived_control - h.actual_control) for h in history] recent_gaps = gaps[-min(5, len(gaps)):] # Confidence based on stability and trend stability = 1.0 - (np.std(recent_gaps) / (np.mean(recent_gaps) + 1e-8)) trend = (recent_gaps[0] - recent_gaps[-1]) / len(recent_gaps) if len(recent_gaps) > 1 else 0 confidence = (stability + max(0, trend)) / 2 return max(0.0, min(1.0, confidence)) async def execute_alignment_cycle(self, tolerance: float = 0.001, max_iterations: int = 1000) -> Dict[str, Dict]: """Execute optimized alignment cycle with early convergence detection""" start_time = time.time() converged_agents = set() adaptive_tolerances = {agent_id: tolerance for agent_id in self.control_models} for iteration in range(max_iterations): self.iteration_count = iteration # Process only non-converged agents active_agents = {aid: model for aid, model in self.control_models.items() if aid not in converged_agents} if not active_agents: break # All agents converged agent_tasks = [] for agent_id, model in active_agents.items(): current_tolerance = adaptive_tolerances[agent_id] agent_tasks.append(self._process_agent_alignment(agent_id, model, current_tolerance)) cycle_results = await asyncio.gather(*agent_tasks) # Update convergence status with early detection for result in cycle_results: agent_id = result["agent_id"] current_gap = result["current_gap"] early_converge, new_tolerance = self._detect_early_convergence(agent_id, current_gap, tolerance) adaptive_tolerances[agent_id] = new_tolerance if result["aligned"] or early_converge: converged_agents.add(agent_id) self.convergence_cache[agent_id] = { "confidence": self._calculate_convergence_confidence(agent_id), "iterations_saved": max_iterations - iteration, "final_tolerance": new_tolerance } # Propagate inter-agent influence for agent_id in self.control_models: self._apply_inter_agent_influence(agent_id) return self._generate_optimized_report(start_time, converged_agents) async def _process_agent_alignment(self, agent_id: str, model, tolerance: float) -> Dict: """Execute alignment procedure for single agent with gap tracking""" state = model.get_current_state() current_gap = abs(state.perceived_control - state.actual_control) # Record current state alignment_state = AlignmentState( agent_id=agent_id, coherence_score=1.0 - current_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) aligned = current_gap < tolerance if not aligned: # Execute reality adjustment weights = self._compute_strategy_weights(current_gap) adjustment = await self.reality_interface.adjust_actual_control(state.perceived_control, weights) # Apply adjustment to actual control model.actual_control = adjustment return { "aligned": aligned, "agent_id": agent_id, "current_gap": current_gap } def _generate_optimized_report(self, start_time: float, converged_agents: set) -> Dict: """Generate operational status report with convergence analytics""" report = { "timestamp": time.time(), "total_duration": time.time() - start_time, "total_iterations": self.iteration_count, "converged_agents_count": len(converged_agents), "early_convergence_savings": self._calculate_iteration_savings(), "agent_states": {}, "convergence_analytics": {} } 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, "converged": agent_id in converged_agents } if agent_id in self.convergence_cache: report["convergence_analytics"][agent_id] = self.convergence_cache[agent_id] return report def _calculate_iteration_savings(self) -> Dict: """Calculate performance improvements from early convergence""" total_possible = len(self.control_models) * self.iteration_count actual_used = sum(len(history) for history in self.alignment_histories.values()) if total_possible > 0: savings_ratio = (total_possible - actual_used) / total_possible else: savings_ratio = 0.0 return { "iterations_saved": total_possible - actual_used, "savings_ratio": savings_ratio, "efficiency_gain": f"{savings_ratio * 100:.1f}%" } def get_convergence_metrics(self, agent_id: str) -> Dict: """Retrieve detailed convergence metrics for monitoring""" history = self.alignment_histories.get(agent_id, []) if not history: return {"status": "NO_DATA"} current = history[-1] confidence = self._calculate_convergence_confidence(agent_id) return { "current_gap": abs(current.perceived_control - current.actual_control), "convergence_confidence": confidence, "stability_score": 1.0 - (np.std([abs(h.perceived_control - h.actual_control) for h in history[-5:]]) if len(history) >= 5 else 0), "trend_direction": "converging" if len(history) >= 2 and history[-1].coherence_score > history[-2].coherence_score else "diverging", "iterations_to_converge": len(history) }