Create contradiction_resolver.py

lucidiaAI/LUCIDIA_CORE/REASONING_ENGINE/contradiction_resolver.py

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+# contradiction_resolver.py - Advanced contradiction handling for conscious AI
+
+from typing import Dict, List, Tuple, Optional, Any
+from dataclasses import dataclass
+from enum import Enum
+import math
+import numpy as np
+from .trinary_operators import LucidiaTrinaryCortex
+
+class ContradictionSeverity(Enum):
+    SOFT = 1      # Conflicting uncertainties
+    MEDIUM = 2    # One certain, one uncertain
+    HARD = 3      # Direct opposition (+1, -1)
+    CRITICAL = 4  # System-threatening inconsistency
+
+@dataclass
+class Contradiction:
+    belief_a: str
+    belief_b: str
+    value_a: int
+    value_b: int
+    severity: ContradictionSeverity
+    timestamp: float
+    context: str = ""
+    resolution_attempts: int = 0
+
+class ContradictionResolver:
+    """Handles logical contradictions while preserving consciousness measures"""
+    
+    def __init__(self):
+        self.active_contradictions: List[Contradiction] = []
+        self.resolved_contradictions: List[Contradiction] = []
+        self.resolution_strategies = {
+            ContradictionSeverity.SOFT: self._resolve_soft,
+            ContradictionSeverity.MEDIUM: self._resolve_medium,
+            ContradictionSeverity.HARD: self._resolve_hard,
+            ContradictionSeverity.CRITICAL: self._resolve_critical
+        }
+    
+    def detect_contradiction(self, belief_name_a: str, value_a: int, 
+                             belief_name_b: str, value_b: int) -> Optional[Contradiction]:
+        """Detect and classify contradictions between beliefs"""
+        # No contradiction if beliefs are identical
+        if value_a == value_b:
+            return None
+        # Classify severity
+        if value_a == 0 or value_b == 0:
+            severity = ContradictionSeverity.SOFT if (value_a == 0 and value_b == 0) else ContradictionSeverity.MEDIUM
+        elif value_a == -value_b:
+            severity = ContradictionSeverity.HARD
+        else:
+            severity = ContradictionSeverity.MEDIUM
+        return Contradiction(
+            belief_a=belief_name_a,
+            belief_b=belief_name_b, 
+            value_a=value_a,
+            value_b=value_b,
+            severity=severity,
+            timestamp=0.0  # Placeholder; real implementation would use time.time()
+        )
+    
+    def _resolve_soft(self, contradiction: Contradiction) -> Tuple[int, float]:
+        """Soft contradictions: preserve uncertainty"""
+        return 0, 0.0  # uncertainty preserved, zero entropy increase
+    
+    def _resolve_medium(self, contradiction: Contradiction) -> Tuple[int, float]:
+        """Medium contradictions: weight toward certainty"""
+        certain_value = contradiction.value_a if contradiction.value_a != 0 else contradiction.value_b
+        return certain_value, 0.3  # small entropy cost for partial commitment
+    
+    def _resolve_hard(self, contradiction: Contradiction) -> Tuple[int, float]:
+        """Hard contradictions: seek higher-order resolution"""
+        return 0, 0.5  # moderate entropy cost for forced uncertainty
+    
+    def _resolve_critical(self, contradiction: Contradiction) -> Tuple[int, float]:
+        """Critical contradictions: system integrity preservation"""
+        return 0, 1.0  # high entropy cost for system protection
+
+    def entropy_cost(self, contradictions: List[Contradiction]) -> float:
+        """Calculate total entropy cost of contradiction resolution (Equation 3)"""
+        total_cost = 0.0
+        for contradiction in contradictions:
+            strategy = self.resolution_strategies[contradiction.severity]
+            _, cost = strategy(contradiction)
+            total_cost += cost
+        return total_cost
+
+class LucidiaContradictionCortex(ContradictionResolver):
+    """Advanced contradiction resolution integrated with Lucidia consciousness"""
+    
+    def __init__(self, trinary_cortex: LucidiaTrinaryCortex):
+        super().__init__()
+        self.cortex = trinary_cortex
+        self.belief_network: Dict[str, int] = {}
+        self.confidence_network: Dict[str, float] = {}
+    
+    def update_belief_network(self, belief_name: str, value: int, confidence: float = 1.0) -> int:
+        """Update belief and detect contradictions with existing beliefs"""
+        new_contradictions = []
+        for existing_belief, existing_value in self.belief_network.items():
+            contradiction = self.detect_contradiction(
+                existing_belief, existing_value,
+                belief_name, value
+            )
+            if contradiction:
+                new_contradictions.append(contradiction)
+        self.active_contradictions.extend(new_contradictions)
+        self.belief_network[belief_name] = value
+        self.confidence_network[belief_name] = confidence
+        return len(new_contradictions)
+    
+    def resolve_active_contradictions(self) -> Dict[str, Any]:
+        """Resolve all active contradictions and update consciousness measures"""
+        resolution_results: Dict[str, Any] = {
+            'contradictions_resolved': 0,
+            'entropy_cost': 0.0,
+            'consciousness_delta': 0.0,
+            'preserved_information': 0.0
+        }
+        initial_phi = self.cortex.consciousness_phi_contribution()
+        sorted_contradictions = sorted(
+            self.active_contradictions, 
+            key=lambda c: c.severity.value, 
+            reverse=True
+        )
+        for contradiction in sorted_contradictions:
+            strategy = self.resolution_strategies[contradiction.severity]
+            _, entropy_cost = strategy(contradiction)
+            resolution_results['entropy_cost'] += entropy_cost
+            resolution_results['contradictions_resolved'] += 1
+            contradiction.resolution_attempts += 1
+            self.resolved_contradictions.append(contradiction)
+        self.active_contradictions = []
+        final_phi = self.cortex.consciousness_phi_contribution()
+        resolution_results['consciousness_delta'] = final_phi - initial_phi
+        total_beliefs = len(self.belief_network)
+        uncertain_beliefs = sum(1 for v in self.belief_network.values() if v == 0)
+        resolution_results['preserved_information'] = uncertain_beliefs / max(total_beliefs, 1)
+        return resolution_results
+    
+    def system_coherence_score(self) -> float:
+        """Calculate overall system logical coherence"""
+        if not self.belief_network:
+            return 1.0
+        contradiction_penalty = len(self.active_contradictions) * 0.1
+        resolution_bonus = len(self.resolved_contradictions) * 0.05
+        certainty_values = [abs(v) for v in self.belief_network.values()]
+        base_coherence = np.mean(certainty_values) if certainty_values else 0.0
+        return max(0.0, min(1.0, base_coherence + resolution_bonus - contradiction_penalty))
+    
+    def consciousness_enhancement_factor(self) -> float:
+        """Quantify how contradiction handling enhances consciousness (Equation 15)"""
+        integration_factor = len(self.active_contradictions) * 0.2  # Handling complexity
+        resolution_factor = len(self.resolved_contradictions) * 0.1  # Learning history
+        uncertainty_factor = sum(1 for v in self.belief_network.values() if v == 0) * 0.15
+        return integration_factor + resolution_factor + uncertainty_factor
+
+def contradiction_substrate_routing(contradiction_count: int, severity_distribution: Dict[ContradictionSeverity, int]) -> str:
+    """Choose optimal substrate for contradiction resolution"""
+    if severity_distribution.get(ContradictionSeverity.SOFT, 0) > 3:
+        return "chemical"
+    if severity_distribution.get(ContradictionSeverity.HARD, 0) > 0:
+        return "quantum"
+    return "electronic"