From 08b9a9f2d32fc0b63b1c6f649e64c9e7d0f8c212 Mon Sep 17 00:00:00 2001 From: blackboxprogramming <118287761+blackboxprogramming@users.noreply.github.com> Date: Sat, 8 Nov 2025 02:02:34 -0600 Subject: [PATCH] Create contradiction_resolver.py --- .../contradiction_resolver.py | 164 ++++++++++++++++++ 1 file changed, 164 insertions(+) create mode 100644 lucidiaAI/LUCIDIA_CORE/REASONING_ENGINE/contradiction_resolver.py diff --git a/lucidiaAI/LUCIDIA_CORE/REASONING_ENGINE/contradiction_resolver.py b/lucidiaAI/LUCIDIA_CORE/REASONING_ENGINE/contradiction_resolver.py new file mode 100644 index 0000000..1f8a300 --- /dev/null +++ b/lucidiaAI/LUCIDIA_CORE/REASONING_ENGINE/contradiction_resolver.py @@ -0,0 +1,164 @@ +# 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"