Create Cortex/neurons/reasoning.py

Cortex/neurons/reasoning.py

@@ -0,0 +1,239 @@
+#!/usr/bin/env python3
+"""
+Neurone de Raisonnement Avancé
+Logique multi-cadres, analyse stratégique, prise de décision
+"""
+
+import asyncio
+import logging
+import random
+from typing import Dict, Any, List, Tuple
+from enum import Enum
+
+class ReasoningFramework(Enum):
+    """Cadres de raisonnement disponibles"""
+    SWOT = "swot_analysis"
+    STRATEGIC = "strategic_planning"
+    LOGICAL = "logical_deduction"
+    SYSTEMIC = "systemic_thinking"
+    CRITICAL = "critical_thinking"
+    CREATIVE = "creative_problem_solving"
+
+class ReasoningNeuron:
+    """
+    Neurone de raisonnement avancé avec multiples cadres d'analyse
+    """
+    
+    def __init__(self):
+        self.logger = logging.getLogger("reasoning_neuron")
+        self.frameworks = list(ReasoningFramework)
+        self.knowledge_base = {}
+        self.thinking_models = {}
+        self.analysis_cache = {}
+        
+    async def initialize(self):
+        """Initialise le neurone de raisonnement"""
+        self.logger.info("🧠 Initialisation du neurone de raisonnement...")
+        
+        # Chargement des modèles de pensée
+        await self._load_thinking_models()
+        await self._load_analysis_patterns()
+        
+        self.logger.info("✅ Neurone de raisonnement initialisé")
+        return True
+
+    async def analyze(self, data: Dict, context: Dict, depth: int = 3) -> Dict[str, Any]:
+        """
+        Analyse les données avec multiples cadres de raisonnement
+        """
+        self.logger.info(f"🔍 Analyse de raisonnement (profondeur: {depth})")
+        
+        # Sélection des cadres basée sur le contexte
+        selected_frameworks = self._select_frameworks(data, context, depth)
+        
+        analyses = {}
+        for framework in selected_frameworks:
+            analysis_method = getattr(self, f"_analyze_{framework.value}")
+            analyses[framework.value] = await analysis_method(data, context, depth)
+        
+        # Synthèse des analyses
+        synthesis = await self._synthesize_analyses(analyses, data, context)
+        
+        return {
+            'frameworks_used': [f.value for f in selected_frameworks],
+            'detailed_analysis': analyses,
+            'synthesis': synthesis,
+            'certainty': self._calculate_certainty(analyses),
+            'coherence_score': await self._calculate_coherence(analyses),
+            'recommendations': await self._generate_recommendations(synthesis),
+            'risk_assessment': await self._assess_risks(analyses),
+            'decision_path': await self._trace_decision_path(analyses)
+        }
+
+    async def _analyze_swot_analysis(self, data: Dict, context: Dict, depth: int) -> Dict:
+        """Analyse SWOT approfondie"""
+        return {
+            'strengths': await self._identify_strengths(data, context, depth),
+            'weaknesses': await self._identify_weaknesses(data, context, depth),
+            'opportunities': await self._identify_opportunities(data, context, depth),
+            'threats': await self._identify_threats(data, context, depth),
+            'strategic_implications': await self._derive_strategic_implications(data, context),
+            'confidence': random.uniform(0.7, 0.95)
+        }
+
+    async def _analyze_strategic_planning(self, data: Dict, context: Dict, depth: int) -> Dict:
+        """Planification stratégique avancée"""
+        return {
+            'vision': await self._define_strategic_vision(data, context),
+            'objectives': await self._set_strategic_objectives(data, context, depth),
+            'action_plan': await self._create_action_plan(data, context),
+            'success_metrics': await self._define_success_metrics(data),
+            'timeline': await self._create_strategic_timeline(data, context),
+            'resource_allocation': await self._allocate_resources_strategically(data),
+            'confidence': random.uniform(0.6, 0.9)
+        }
+
+    async def _analyze_logical_deduction(self, data: Dict, context: Dict, depth: int) -> Dict:
+        """Déduction logique formelle"""
+        premises = await self._extract_premises(data, context)
+        conclusions = await self._deduce_conclusions(premises, depth)
+        
+        return {
+            'premises': premises,
+            'inferences': await self._make_inferences(premises, depth),
+            'conclusions': conclusions,
+            'logical_consistency': await self._check_logical_consistency(conclusions),
+            'fallacies_detected': await self._detect_logical_fallacies(premises),
+            'confidence': random.uniform(0.8, 0.98)
+        }
+
+    async def _analyze_systemic_thinking(self, data: Dict, context: Dict, depth: int) -> Dict:
+        """Pensée systémique"""
+        return {
+            'system_components': await self._identify_system_components(data, context),
+            'interconnections': await self._map_interconnections(data, context, depth),
+            'feedback_loops': await self._identify_feedback_loops(data, context),
+            'emergent_properties': await self._analyze_emergent_properties(data, context),
+            'system_dynamics': await self._model_system_dynamics(data, context),
+            'leverage_points': await self._identify_leverage_points(data, context),
+            'confidence': random.uniform(0.7, 0.92)
+        }
+
+    async def _synthesize_analyses(self, analyses: Dict, data: Dict, context: Dict) -> Dict:
+        """Synthétise les analyses de tous les cadres"""
+        synthesis = {
+            'key_insights': [],
+            'critical_factors': [],
+            'strategic_direction': '',
+            'risk_level': 'medium',
+            'opportunity_areas': [],
+            'decision_framework': {}
+        }
+        
+        # Agrégation des insights
+        for framework, analysis in analyses.items():
+            if 'strengths' in analysis:
+                synthesis['key_insights'].extend(analysis['strengths'][:2])
+            if 'opportunities' in analysis:
+                synthesis['key_insights'].extend(analysis['opportunities'][:2])
+                synthesis['opportunity_areas'].extend(analysis['opportunities'][:3])
+            if 'threats' in analysis:
+                synthesis['critical_factors'].extend(analysis['threats'][:2])
+            if 'conclusions' in analysis:
+                synthesis['key_insights'].extend(analysis['conclusions'][:2])
+        
+        # Détermination de la direction stratégique
+        synthesis['strategic_direction'] = await self._determine_strategic_direction(analyses)
+        
+        # Évaluation des risques
+        synthesis['risk_level'] = await self._assess_overall_risk(analyses)
+        
+        # Cadre de décision
+        synthesis['decision_framework'] = await self._build_decision_framework(analyses)
+        
+        return synthesis
+
+    def _calculate_certainty(self, analyses: Dict) -> float:
+        """Calcule la certitude globale"""
+        confidence_scores = []
+        for analysis in analyses.values():
+            if 'confidence' in analysis:
+                confidence_scores.append(analysis['confidence'])
+        
+        return sum(confidence_scores) / len(confidence_scores) if confidence_scores else 0.7
+
+    async def _calculate_coherence(self, analyses: Dict) -> float:
+        """Calcule le score de cohérence entre les analyses"""
+        # Logique simplifiée pour l'exemple
+        return min(0.95, 0.7 + (len(analyses) * 0.05))
+
+    def _select_frameworks(self, data: Dict, context: Dict, depth: int) -> List[ReasoningFramework]:
+        """Sélectionne les cadres de raisonnement appropriés"""
+        # Logique de sélection basée sur le type de problème
+        problem_type = data.get('type', 'general')
+        
+        if problem_type in ['strategic', 'planning']:
+            return [ReasoningFramework.STRATEGIC, ReasoningFramework.SYSTEMIC, ReasoningFramework.SWOT]
+        elif problem_type in ['logical', 'analytical']:
+            return [ReasoningFramework.LOGICAL, ReasoningFramework.CRITICAL]
+        elif problem_type in ['creative', 'innovation']:
+            return [ReasoningFramework.CREATIVE, ReasoningFramework.SYSTEMIC]
+        else:
+            # Sélection par défaut
+            return random.sample(self.frameworks, min(depth, len(self.frameworks)))
+
+    # Méthodes d'implémentation (simplifiées pour l'exemple)
+    async def _load_thinking_models(self):
+        """Charge les modèles de pensée"""
+        self.thinking_models = {
+            'first_principles': {
+                'description': "Décomposition aux principes fondamentaux",
+                'steps': ['identify_assumptions', 'break_down_fundamentals', 'rebuild_from_scratch']
+            },
+            'inversion_thinking': {
+                'description': "Résolution par inversion du problème",
+                'steps': ['define_opposite_goal', 'identify_prevention_measures', 'invert_solution']
+            }
+        }
+
+    async def _load_analysis_patterns(self):
+        """Charge les patterns d'analyse"""
+        self.analysis_patterns = {
+            'problem_solving': ['define', 'analyze', 'generate', 'evaluate', 'implement'],
+            'decision_making': ['options', 'criteria', 'evaluation', 'selection', 'execution']
+        }
+
+    async def _identify_strengths(self, data: Dict, context: Dict, depth: int) -> List[str]:
+        return ["Capacité d'analyse multidimensionnelle", "Accès à des connaissances étendues"]
+
+    async def _identify_weaknesses(self, data: Dict, context: Dict, depth: int) -> List[str]:
+        return ["Dépendance à la qualité des données d'entrée", "Complexité des problèmes ambigus"]
+
+    async def _identify_opportunities(self, data: Dict, context: Dict, depth: int) -> List[str]:
+        return ["Amélioration continue par l'apprentissage", "Synergies avec d'autres systèmes"]
+
+    async def _identify_threats(self, data: Dict, context: Dict, depth: int) -> List[str]:
+        return ["Biais potentiels dans l'analyse", "Variables externes imprévisibles"]
+
+    async def _generate_recommendations(self, synthesis: Dict) -> List[str]:
+        return [
+            "Adopter une approche équilibrée intégrant multiples perspectives",
+            "Mettre en place des mécanismes de feedback continu",
+            "Planifier des revues stratégiques périodiques"
+        ]
+
+    async def _assess_risks(self, analyses: Dict) -> Dict:
+        return {
+            'level': 'medium',
+            'mitigation_strategies': ['Surveillance active', 'Plans de contingence'],
+            'monitoring_metrics': ['Indicateurs de performance', 'Signaux d'alerte précoce']
+        }
+
+    async def get_status(self) -> Dict[str, Any]:
+        """Retourne le statut du neurone"""
+        return {
+            'active': True,
+            'frameworks_loaded': len(self.frameworks),
+            'thinking_models': len(self.thinking_models),
+            'analysis_cache_size': len(self.analysis_cache)
+        }