Create Cortex/core/cognitive_architecture.py

Cortex/core/cognitive_architecture.py

@@ -0,0 +1,593 @@
+#!/usr/bin/env python3
+"""
+Architecture Cognitive Unifiée Barouia-Cortex Ultimate
+Intégration de tous les modules en un système cognitif cohérent
+"""
+
+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)