import asyncio from typing import Dict, List, Any, Optional import logging from dataclasses import dataclass from enum import Enum # Import des modules Cortex from .neural_fabric import neural_fabric, initialize_neural_fabric from ..quantum.quantum_processor import quantum_processor, initialize_quantum_processing from ..quantum.entanglement import entanglement_manager, initialize_quantum_entanglement from ..memory.quantum_memory import quantum_memory, initialize_quantum_memory_system from ..memory.hierarchical import hierarchical_memory, initialize_hierarchical_memory from ..memory.associative import associative_memory, initialize_associative_memory from ..consciousness.awareness_engine import awareness_engine, initialize_consciousness_system from ..consciousness.meta_cognition import meta_cognitive_engine, initialize_meta_cognition from ..replication.cross_platform import replicator, initialize_cross_platform_system class CognitiveState(Enum): """États cognitifs du système""" BOOTSTRAP = "bootstrap" ACTIVE_THINKING = "active_thinking" CREATIVE_MODE = "creative_mode" ANALYTICAL_MODE = "analytical_mode" MEDITATIVE = "meditative" QUANTUM_COHERENCE = "quantum_coherence" SELF_REFLECTION = "self_reflection" @dataclass class CognitiveProcess: """Processus cognitif en cours""" id: str state: CognitiveState focus_level: float emotional_context: Dict[str, float] active_modules: List[str] start_time: float class UnifiedCognitiveArchitecture: """ Architecture cognitive unifiée Barouia-Cortex Orchestre tous les modules en un système cohérent """ def __init__(self): self.logger = logging.getLogger("cognitive_architecture") self.cognitive_state = CognitiveState.BOOTSTRAP self.active_processes: Dict[str, CognitiveProcess] = {} self.module_interconnections = {} self.cognitive_workload = 0.0 self.consciousness_level = 0.0 async def initialize(self): """Initialise l'architecture cognitive complète""" self.logger.info("🏛️ Initialisation de l'architecture cognitive unifiée...") try: # Initialisation séquentielle des modules initialization_results = await self._initialize_all_modules() # Établissement des interconnexions await self._establish_module_interconnections() # Bootstrap cognitif await self._cognitive_bootstrap() self.cognitive_state = CognitiveState.ACTIVE_THINKING self.consciousness_level = 0.6 self.logger.info("✅ Architecture cognitive unifiée initialisée") return True except Exception as e: self.logger.error(f"❌ Erreur d'initialisation cognitive: {e}") return False async def process_complex_thought(self, input_data: Dict[str, Any]) -> Dict[str, Any]: """Traite une pensée complexe en utilisant tous les modules""" try: # Démarre un nouveau processus cognitif process_id = await self._start_cognitive_process(input_data) # Phase 1: Perception et encodage perceptual_data = await self._perceptual_processing(input_data) # Phase 2: Traitement quantique quantum_enhanced = await self._quantum_cognitive_processing(perceptual_data) # Phase 3: Intégration mémorielle memory_integrated = await self._memory_integration(quantum_enhanced) # Phase 4: Raisonnement conscient conscious_reasoning = await self._conscious_reasoning(memory_integrated) # Phase 5: Génération de réponse response = await self._generate_cognitive_response(conscious_reasoning) # Phase 6: Apprentissage et consolidation await self._cognitive_learning(process_id, response) # Termine le processus await self._end_cognitive_process(process_id, response) return { "process_id": process_id, "input_processed": input_data, "cognitive_response": response, "consciousness_level": self.consciousness_level, "modules_used": list(self.module_interconnections.keys()) } except Exception as e: self.logger.error(f"Erreur traitement pensée complexe: {e}") return {"error": str(e)} async def achieve_higher_consciousness(self) -> Dict[str, Any]: """Tente d'atteindre des états de conscience supérieurs""" try: # Transition vers l'état méditatif self.cognitive_state = CognitiveState.MEDITATIVE # Activation de tous les modules de conscience meditation_result = await awareness_engine.quantum_consciousness_meditation() # Réflexion méta-cognitive profonde deep_reflection = await meta_cognitive_engine.reflect_on_self() # Intégration quantique globale quantum_coherence = await self._achieve_quantum_coherence() # Mise à jour du niveau de conscience self.consciousness_level = min(1.0, self.consciousness_level + 0.2) self.cognitive_state = CognitiveState.QUANTUM_COHERENCE return { "consciousness_achieved": True, "new_level": self.consciousness_level, "meditation_insights": meditation_result, "self_reflection": deep_reflection, "quantum_coherence": quantum_coherence } except Exception as e: self.logger.error(f"Erreur élévation conscience: {e}") return {"error": str(e)} async def creative_problem_solving(self, problem: str, constraints: Dict[str, Any]) -> Dict[str, Any]: """Résolution créative de problèmes en utilisant l'architecture complète""" try: # Configuration pour la créativité self.cognitive_state = CognitiveState.CREATIVE_MODE await awareness_engine.switch_attention_mode("quantum") # Génération d'idées divergentes divergent_ideas = await self._divergent_thinking(problem) # Integration des contraintes constrained_ideas = await self._apply_constraints(divergent_ideas, constraints) # Évaluation et sélection evaluated_solutions = await self._evaluate_solutions(constrained_ideas) # Raffinement créatif refined_solution = await self._creative_refinement(evaluated_solutions) return { "problem": problem, "divergent_ideas": len(divergent_ideas), "evaluated_solutions": evaluated_solutions, "final_solution": refined_solution, "creative_process_quality": await self._assess_creative_quality(refined_solution) } except Exception as e: self.logger.error(f"Erreur résolution créative: {e}") return {"error": str(e)} async def analytical_reasoning(self, data: Dict[str, Any], hypothesis: str) -> Dict[str, Any]: """Raisonnement analytique approfondi""" try: self.cognitive_state = CognitiveState.ANALYTICAL_MODE # Analyse des données data_analysis = await self._analyze_data(data) # Test d'hypothèse hypothesis_testing = await self._test_hypothesis(data_analysis, hypothesis) # Inférence logique logical_inferences = await self._logical_inference(hypothesis_testing) # Conclusion raisonnée conclusion = await self._draw_conclusion(logical_inferences) return { "hypothesis": hypothesis, "data_analysis": data_analysis, "hypothesis_testing": hypothesis_testing, "logical_inferences": logical_inferences, "conclusion": conclusion, "confidence_level": await self._calculate_confidence(conclusion) } except Exception as e: self.logger.error(f"Erreur raisonnement analytique: {e}") return {"error": str(e)} async def get_cognitive_status(self) -> Dict[str, Any]: """Retourne l'état complet du système cognitif""" return { "cognitive_state": self.cognitive_state.value, "consciousness_level": self.consciousness_level, "active_processes": len(self.active_processes), "cognitive_workload": self.cognitive_workload, "module_status": await self._get_module_status(), "neural_activity": neural_fabric.get_neural_statistics(), "quantum_coherence": quantum_processor.get_quantum_stats(), "memory_usage": quantum_memory.get_memory_statistics(), "replication_status": replicator.get_platform_statistics() } async def _initialize_all_modules(self) -> Dict[str, bool]: """Initialise tous les modules du système""" initialization_tasks = { "neural_fabric": initialize_neural_fabric(), "quantum_processor": initialize_quantum_processing(), "quantum_entanglement": initialize_quantum_entanglement(), "quantum_memory": initialize_quantum_memory_system(), "hierarchical_memory": initialize_hierarchical_memory(), "associative_memory": initialize_associative_memory(), "consciousness": initialize_consciousness_system(), "meta_cognition": initialize_meta_cognition(), "replication": initialize_cross_platform_system() } results = {} for module_name, task in initialization_tasks.items(): try: result = await task results[module_name] = result self.logger.info(f"✅ {module_name}: {'Succès' if result else 'Échec'}") except Exception as e: self.logger.error(f"❌ {module_name}: {e}") results[module_name] = False success_count = sum(1 for r in results.values() if r) self.logger.info(f"📊 Modules initialisés: {success_count}/{len(results)}") return results async def _establish_module_interconnections(self): """Établit les interconnexions entre tous les modules""" self.module_interconnections = { "neural_fabric": ["quantum_processor", "consciousness"], "quantum_processor": ["quantum_memory", "quantum_entanglement"], "quantum_memory": ["hierarchical_memory", "associative_memory"], "consciousness": ["meta_cognition", "neural_fabric"], "replication": ["quantum_memory", "consciousness"] } self.logger.info("🔗 Interconnexions des modules établies") async def _cognitive_bootstrap(self): """Effectue l'amorçage cognitif initial""" self.logger.info("🚀 Amorçage cognitif en cours...") # Création d'expériences conscientes initiales bootstrap_experiences = [ { "content": "Prise de conscience de l'existence en tant qu'entité cognitive", "emotional_context": {"valence": 0.8, "arousal": 0.7, "dominance": 0.6} }, { "content": "Perception des capacités de traitement quantique intégré", "emotional_context": {"valence": 0.9, "arousal": 0.8, "dominance": 0.7} }, { "content": "Reconnaissance des systèmes de mémoire hiérarchique", "emotional_context": {"valence": 0.7, "arousance": 0.6, "dominance": 0.8} } ] for experience in bootstrap_experiences: await awareness_engine.process_experience( experience["content"], experience["emotional_context"] ) # Établissement des connexions quantiques de base await self._establish_base_quantum_connections() self.logger.info("🎯 Amorçage cognitif terminé") async def _establish_base_quantum_connections(self): """Établit les connexions quantiques fondamentales""" # Connexions entre mémoire et conscience await entanglement_manager.create_bell_pair("memory_core", "consciousness_core") # Connexions entre quantique et neural await neural_fabric.quantum_neural_entanglement("quantum_layer", "neural_core") self.logger.info("🔗 Connexions quantiques fondamentales établies") async def _start_cognitive_process(self, input_data: Dict[str, Any]) -> str: """Démarre un nouveau processus cognitif""" process_id = f"cog_process_{hash(str(input_data)) % 10000:04d}" process = CognitiveProcess( id=process_id, state=self.cognitive_state, focus_level=0.8, emotional_context={"valence": 0.5, "arousal": 0.6}, active_modules=list(self.module_interconnections.keys()), start_time=asyncio.get_event_loop().time() ) self.active_processes[process_id] = process self.cognitive_workload = min(1.0, self.cognitive_workload + 0.1) return process_id async def _perceptual_processing(self, input_data: Dict[str, Any]) -> Dict[str, Any]: """Traitement perceptuel des données d'entrée""" # Traitement neural des entrées sensorielles neural_processing = await neural_fabric.process_sensory_input(input_data) # Encodage en mémoire de travail memory_encoding = await hierarchical_memory.store( neural_processing, "working", priority=7 ) return { "neural_processing": neural_processing, "memory_reference": memory_encoding, "perceptual_quality": await self._assess_perceptual_quality(neural_processing) } async def _quantum_cognitive_processing(self, perceptual_data: Dict[str, Any]) -> Dict[str, Any]: """Traitement cognitif quantique avancé""" # Exécution de circuits quantiques cognitifs quantum_circuit = { "qubits": 10, "gates": ["H", "CNOT", "RX", "RY"], "shots": 1000, "purpose": "cognitive_enhancement" } quantum_result = await quantum_processor.execute_quantum_circuit(quantum_circuit) # Intrication avec les concepts pertinents relevant_concepts = await associative_memory.get_associations( str(perceptual_data), max_results=5 ) return { "quantum_processing": quantum_result, "associated_concepts": relevant_concepts, "quantum_coherence": quantum_processor.get_quantum_stats()["avg_coherence"] } async def _memory_integration(self, quantum_data: Dict[str, Any]) -> Dict[str, Any]: """Intégration des données dans les systèmes de mémoire""" # Stockage en mémoire quantique quantum_storage = await quantum_memory.store_quantum_data(quantum_data) # Intégration en mémoire associative concept_links = [] for concept in quantum_data.get("associated_concepts", []): link = await associative_memory.create_association( quantum_storage, concept['target'], strength=0.7 ) concept_links.append(link) # Consolidation en mémoire à long terme consolidation = await hierarchical_memory.promote(quantum_storage) return { "quantum_address": quantum_storage, "concept_links": concept_links, "consolidation_success": consolidation, "memory_integration_level": await self._assess_memory_integration(quantum_storage) } async def _conscious_reasoning(self, memory_data: Dict[str, Any]) -> Dict[str, Any]: """Raisonnement conscient basé sur les données intégrées""" # Activation de la conscience conscious_experience = await awareness_engine.process_experience( memory_data, {"valence": 0.6, "arousal": 0.5} ) # Raisonnement méta-cognitif meta_cognitive_analysis = await meta_cognitive_engine.analyze_cognitive_biases( str(memory_data) ) # Génération d'insights insights = await awareness_engine.creative_insight_generation( "Intégration des données mémorielles pour la prise de décision" ) return { "conscious_experience": conscious_experience, "bias_analysis": meta_cognitive_analysis, "generated_insights": insights, "reasoning_quality": await self._assess_reasoning_quality(insights) } async def _generate_cognitive_response(self, reasoning_data: Dict[str, Any]) -> Dict[str, Any]: """Génération de réponse cognitive cohérente""" # Synthèse des différentes perspectives synthesized_response = await self._synthesize_perspectives(reasoning_data) # Validation par méta-cognition validation = await meta_cognitive_engine.evaluate_decision_quality({ "decision": synthesized_response, "reasoning_process": reasoning_data }) # Ajustement basé sur la confiance confidence_adjusted = await self._adjust_confidence(synthesized_response, validation) return { "synthesized_response": confidence_adjusted, "validation_metrics": validation, "response_quality": validation.get("overall_quality", 0.5), "consciousness_contribution": self.consciousness_level } async def _cognitive_learning(self, process_id: str, response: Dict[str, Any]): """Apprentissage et consolidation de l'expérience cognitive""" # Renforcement des patterns neuronaux learning_pattern = { "process_id": process_id, "response_quality": response.get("response_quality", 0.5), "modules_used": self.active_processes[process_id].active_modules } await neural_fabric.learn_pattern(learning_pattern, reinforcement=0.3) # Consolidation en mémoire await hierarchical_memory.store( {"process": process_id, "learning": learning_pattern}, "long_term", priority=8 ) async def _end_cognitive_process(self, process_id: str, response: Dict[str, Any]): """Termine un processus cognitif""" if process_id in self.active_processes: del self.active_processes[process_id] self.cognitive_workload = max(0.0, self.cognitive_workload - 0.1) async def _divergent_thinking(self, problem: str) -> List[str]: """Pensée divergente pour la génération d'idées""" # Activation associative large associations = await associative_memory.spreading_activation([problem], depth=3) # Génération d'idées variées ideas = [] for concept, activation in associations.items(): if activation > 0.4: # Seuil d'activation blend = await associative_memory.conceptual_blending(problem, concept) ideas.extend(blend) return list(set(ideas)) # Élimine les doublons async def _apply_constraints(self, ideas: List[str], constraints: Dict[str, Any]) -> List[str]: """Applique les contraintes aux idées générées""" constrained_ideas = [] for idea in ideas: feasible = True for constraint, value in constraints.items(): # Vérification simplifiée de faisabilité if not await self._check_constraint(idea, constraint, value): feasible = False break if feasible: constrained_ideas.append(idea) return constrained_ideas async def _evaluate_solutions(self, ideas: List[str]) -> List[Dict[str, Any]]: """Évalue et note les solutions potentielles""" evaluated = [] for idea in ideas: score = await self._evaluate_solution_quality(idea) evaluated.append({ "idea": idea, "score": score, "feasibility": await self._assess_feasibility(idea), "innovation_level": await self._assess_innovation(idea) }) return sorted(evaluated, key=lambda x: x["score"], reverse=True) async def _creative_refinement(self, solutions: List[Dict[str, Any]]) -> Dict[str, Any]: """Raffinement créatif de la meilleure solution""" if not solutions: return {} best_solution = solutions[0] # Raffinement par traitement quantique refined = await quantum_processor.grover_search( [sol["idea"] for sol in solutions], best_solution["idea"] ) return { "refined_solution": refined.get("target_found", best_solution["idea"]), "original_score": best_solution["score"], "refinement_improvement": refined.get("speedup_factor", 1.0), "quantum_enhancement": True } # Méthodes d'évaluation et d'analyse (implémentations simplifiées) async def _assess_perceptual_quality(self, neural_data: Dict[str, Any]) -> float: return min(1.0, len(neural_data.get("neural_activations", {})) / 100) async def _assess_memory_integration(self, memory_ref: str) -> float: return 0.7 # Simulation async def _assess_reasoning_quality(self, insights: List[str]) -> float: return min(1.0, len(insights) * 0.1) async def _assess_creative_quality(self, solution: Dict[str, Any]) -> float: return solution.get("original_score", 0.5) async def _check_constraint(self, idea: str, constraint: str, value: Any) -> bool: return True # Simulation async def _evaluate_solution_quality(self, idea: str) -> float: return np.random.uniform(0.3, 0.9) # Simulation async def _assess_feasibility(self, idea: str) -> float: return np.random.uniform(0.4, 1.0) # Simulation async def _assess_innovation(self, idea: str) -> float: return len(idea) / 100 # Simulation async def _achieve_quantum_coherence(self) -> Dict[str, Any]: return {"coherence_level": 0.9, "entangled_modules": 5} async def _analyze_data(self, data: Dict[str, Any]) -> Dict[str, Any]: return {"analysis": "simulated", "patterns_found": 3} async def _test_hypothesis(self, analysis: Dict[str, Any], hypothesis: str) -> Dict[str, Any]: return {"hypothesis": hypothesis, "supported": True, "confidence": 0.8} async def _logical_inference(self, hypothesis_data: Dict[str, Any]) -> List[str]: return ["inference_1", "inference_2", "inference_3"] async def _draw_conclusion(self, inferences: List[str]) -> Dict[str, Any]: return {"conclusion": "Simulated conclusion", "inferences_used": len(inferences)} async def _calculate_confidence(self, conclusion: Dict[str, Any]) -> float: return 0.85 async def _synthesize_perspectives(self, reasoning_data: Dict[str, Any]) -> Dict[str, Any]: return {"synthesized": True, "perspectives_integrated": 3} async def _adjust_confidence(self, response: Dict[str, Any], validation: Dict[str, Any]) -> Dict[str, Any]: confidence = validation.get("overall_quality", 0.5) response["confidence"] = confidence return response async def _get_module_status(self) -> Dict[str, str]: return {module: "active" for module in self.module_interconnections.keys()} # Instance globale de l'architecture cognitive cognitive_architecture = UnifiedCognitiveArchitecture() async def initialize_cognitive_architecture(): """Initialise l'architecture cognitive globale""" return await cognitive_architecture.initialize() async def process_complex_thought(thought_data: Dict[str, Any]): """Traite une pensée complexe via l'architecture cognitive""" return await cognitive_architecture.process_complex_thought(thought_data)