1283 lines
62 KiB
JavaScript
1283 lines
62 KiB
JavaScript
/**
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* Complete Enhanced Psycho-Symbolic Reasoning with Full Learning Integration
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* Includes: Domain Adaptation, Creative Reasoning, Enhanced Knowledge Base, Analogical Reasoning
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*/
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import * as crypto from 'crypto';
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import { ReasoningCache } from './reasoning-cache.js';
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// 1. Domain Adaptation Engine - Auto-detect and adapt reasoning styles
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class DomainAdaptationEngine {
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domainPatterns = new Map();
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reasoningStyles = new Map();
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crossDomainMappings = new Map();
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semanticClusters = new Map();
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constructor() {
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this.initializeDomainPatterns();
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this.initializeReasoningStyles();
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this.initializeCrossDomainMappings();
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this.buildSemanticClusters();
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}
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initializeDomainPatterns() {
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this.domainPatterns.set('physics', {
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keywords: ['quantum', 'particle', 'energy', 'field', 'force', 'wave', 'resonance', 'entanglement'],
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reasoning_style: 'mathematical_modeling',
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analogy_domains: ['information_theory', 'consciousness', 'computing']
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});
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this.domainPatterns.set('biology', {
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keywords: ['cell', 'organism', 'evolution', 'genetic', 'ecosystem', 'neural', 'brain'],
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reasoning_style: 'emergent_systems',
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analogy_domains: ['computer_networks', 'social_systems', 'economics']
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});
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this.domainPatterns.set('computer_science', {
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keywords: ['algorithm', 'data', 'network', 'system', 'computation', 'software', 'ai', 'machine', 'learning', 'neural', 'artificial'],
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reasoning_style: 'systematic_analysis',
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analogy_domains: ['biology', 'physics', 'cognitive_science']
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});
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this.domainPatterns.set('consciousness', {
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keywords: ['consciousness', 'awareness', 'mind', 'experience', 'qualia', 'phi'],
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reasoning_style: 'phenomenological',
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analogy_domains: ['physics', 'information_theory', 'complexity_science']
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});
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this.domainPatterns.set('temporal', {
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keywords: ['time', 'temporal', 'sequence', 'causality', 'evolution', 'dynamics'],
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reasoning_style: 'temporal_analysis',
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analogy_domains: ['physics', 'consciousness', 'systems_theory']
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});
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this.domainPatterns.set('art', {
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keywords: ['art', 'artistic', 'painting', 'visual', 'aesthetic', 'creative', 'expression', 'pollock', 'drip', 'canvas', 'color', 'form', 'style', 'composition'],
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reasoning_style: 'aesthetic_synthesis',
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analogy_domains: ['mathematics', 'physics', 'psychology', 'philosophy']
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});
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this.domainPatterns.set('music', {
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keywords: ['music', 'musical', 'sound', 'rhythm', 'melody', 'harmony', 'composition', 'jazz', 'improvisation', 'symphony', 'acoustic', 'tone', 'chord'],
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reasoning_style: 'harmonic_analysis',
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analogy_domains: ['mathematics', 'physics', 'emotion', 'language']
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});
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this.domainPatterns.set('narrative', {
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keywords: ['story', 'narrative', 'plot', 'character', 'fiction', 'novel', 'literary', 'text', 'author', 'dialogue', 'scene', 'chapter'],
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reasoning_style: 'narrative_analysis',
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analogy_domains: ['psychology', 'philosophy', 'sociology', 'linguistics']
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});
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this.domainPatterns.set('philosophy', {
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keywords: ['philosophy', 'philosophical', 'metaphysics', 'ontology', 'epistemology', 'ethics', 'logic', 'existence', 'reality', 'truth'],
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reasoning_style: 'conceptual_analysis',
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analogy_domains: ['logic', 'psychology', 'mathematics', 'consciousness']
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});
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this.domainPatterns.set('emotion', {
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keywords: ['emotion', 'emotional', 'feeling', 'mood', 'sentiment', 'empathy', 'psychology', 'affect', 'resonance'],
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reasoning_style: 'empathetic_reasoning',
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analogy_domains: ['neuroscience', 'art', 'music', 'social_dynamics']
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});
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this.domainPatterns.set('mathematics', {
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keywords: ['mathematical', 'equation', 'function', 'theorem', 'proof', 'geometry', 'algebra', 'calculus', 'topology', 'fractal', 'chaos', 'matrix', 'solving', 'optimization', 'linear', 'algorithm', 'sublinear', 'portfolio', 'finance', 'trading'],
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reasoning_style: 'formal_reasoning',
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analogy_domains: ['physics', 'art', 'music', 'nature']
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});
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// Add financial/economic domain with comprehensive financial terms
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this.domainPatterns.set('finance', {
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keywords: ['finance', 'financial', 'trading', 'portfolio', 'investment', 'market', 'economic', 'risk', 'return', 'asset', 'optimization', 'allocation', 'hedge', 'quant', 'stock', 'stocks', 'crypto', 'cryptocurrency', 'bitcoin', 'bonds', 'equity', 'derivative', 'futures', 'options', 'forex', 'currency', 'commodity', 'etf', 'mutual', 'fund', 'capital', 'valuation', 'pricing', 'yield', 'dividend', 'volatility', 'sharpe', 'alpha', 'beta', 'correlation', 'covariance', 'diversification', 'arbitrage', 'liquidity', 'leverage', 'margin', 'short', 'long', 'bull', 'bear', 'momentum', 'trend', 'technical', 'fundamental', 'analysis', 'backtesting', 'monte', 'carlo', 'black', 'scholes', 'var', 'credit', 'default', 'swap', 'spread', 'duration', 'convexity'],
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reasoning_style: 'quantitative_analysis',
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analogy_domains: ['mathematics', 'computer_science', 'statistics', 'game_theory']
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});
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// Add the missing creative_synthesis domain
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this.domainPatterns.set('creative_synthesis', {
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keywords: ['creative', 'synthesis', 'novel', 'innovation', 'interdisciplinary', 'cross-domain', 'emergent', 'hybrid'],
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reasoning_style: 'creative_synthesis',
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analogy_domains: ['art', 'music', 'philosophy', 'science']
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});
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}
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initializeReasoningStyles() {
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this.reasoningStyles.set('mathematical_modeling', 'Analyze through mathematical relationships and quantitative patterns');
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this.reasoningStyles.set('emergent_systems', 'Focus on emergent properties and self-organization');
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this.reasoningStyles.set('systematic_analysis', 'Break down into components and systematic interactions');
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this.reasoningStyles.set('phenomenological', 'Examine subjective experience and qualitative aspects');
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this.reasoningStyles.set('temporal_analysis', 'Consider temporal dynamics and causal sequences');
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this.reasoningStyles.set('creative_synthesis', 'Generate novel connections across domains');
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this.reasoningStyles.set('aesthetic_synthesis', 'Explore aesthetic relationships, visual harmony, and creative expression patterns');
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this.reasoningStyles.set('harmonic_analysis', 'Analyze rhythmic patterns, melodic structures, and sonic relationships');
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this.reasoningStyles.set('narrative_analysis', 'Follow story structures, character development, and plot dynamics');
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this.reasoningStyles.set('conceptual_analysis', 'Examine abstract concepts, logical relationships, and philosophical implications');
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this.reasoningStyles.set('empathetic_reasoning', 'Consider emotional resonance, human feelings, and psychological impact');
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this.reasoningStyles.set('formal_reasoning', 'Apply logical structures, mathematical proofs, and formal methods');
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this.reasoningStyles.set('quantitative_analysis', 'Apply mathematical models, statistical analysis, and data-driven optimization techniques');
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this.reasoningStyles.set('creative_synthesis', 'Generate novel connections across domains and explore interdisciplinary insights');
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}
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initializeCrossDomainMappings() {
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this.crossDomainMappings.set('physics', ['information_flow', 'energy_transfer', 'field_interactions']);
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this.crossDomainMappings.set('biology', ['network_connectivity', 'adaptive_behavior', 'emergent_intelligence']);
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this.crossDomainMappings.set('consciousness', ['information_integration', 'subjective_experience', 'awareness_levels']);
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this.crossDomainMappings.set('temporal', ['causal_chains', 'temporal_ordering', 'dynamic_evolution']);
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this.crossDomainMappings.set('art', ['visual_patterns', 'aesthetic_harmony', 'creative_expression', 'compositional_balance']);
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this.crossDomainMappings.set('music', ['harmonic_resonance', 'rhythmic_patterns', 'melodic_flow', 'sonic_textures']);
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this.crossDomainMappings.set('narrative', ['story_arcs', 'character_development', 'thematic_elements', 'dramatic_tension']);
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this.crossDomainMappings.set('philosophy', ['conceptual_frameworks', 'logical_structures', 'ethical_implications', 'metaphysical_dimensions']);
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this.crossDomainMappings.set('emotion', ['affective_resonance', 'emotional_dynamics', 'empathetic_connections', 'psychological_impact']);
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this.crossDomainMappings.set('mathematics', ['formal_structures', 'logical_proofs', 'geometric_relationships', 'abstract_patterns']);
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this.crossDomainMappings.set('finance', ['quantitative_models', 'risk_optimization', 'portfolio_theory', 'statistical_arbitrage']);
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}
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buildSemanticClusters() {
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// Build semantic clusters for enhanced search
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this.semanticClusters.set('consciousness', ['awareness', 'experience', 'mind', 'cognition', 'qualia']);
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this.semanticClusters.set('quantum', ['probabilistic', 'superposition', 'entanglement', 'coherence']);
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this.semanticClusters.set('neural', ['network', 'brain', 'neuron', 'synapse', 'learning']);
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this.semanticClusters.set('temporal', ['time', 'sequence', 'causality', 'evolution', 'dynamics']);
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this.semanticClusters.set('emergence', ['complexity', 'self-organization', 'phase-transition', 'novelty']);
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// Creative domain clusters
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this.semanticClusters.set('art', ['painting', 'visual', 'aesthetic', 'creative', 'expression', 'color', 'form', 'composition', 'style']);
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this.semanticClusters.set('music', ['harmony', 'rhythm', 'melody', 'sound', 'tone', 'composition', 'resonance', 'frequency']);
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this.semanticClusters.set('narrative', ['story', 'character', 'plot', 'theme', 'meaning', 'structure', 'narrative']);
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this.semanticClusters.set('philosophy', ['concept', 'logic', 'ethics', 'metaphysics', 'knowledge', 'truth', 'reality']);
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this.semanticClusters.set('emotion', ['feeling', 'affective', 'psychological', 'empathy', 'resonance', 'connection']);
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this.semanticClusters.set('mathematics', ['formal', 'logical', 'proof', 'structure', 'pattern', 'relationship', 'abstract']);
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}
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detectDomains(query, concepts) {
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const detectedDomains = [];
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const queryLower = query.toLowerCase();
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const allTerms = [queryLower, ...concepts.map(c => c.toLowerCase())];
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console.log('DEBUG: Domain detection called with:', { query, concepts, allTerms });
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console.log('DEBUG: Available domains:', Array.from(this.domainPatterns.keys()));
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// Score-based domain detection for better accuracy
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for (const [domain, pattern] of this.domainPatterns) {
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let score = 0;
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const matches = [];
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pattern.keywords.forEach((keyword) => {
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allTerms.forEach(term => {
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const keywordLower = keyword.toLowerCase();
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if (term.includes(keywordLower)) {
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// Exact matches get higher score
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if (term === keywordLower) {
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score += 3.0; // Increased from 2.0
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matches.push(`exact:${keyword}`);
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}
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else if (term.includes(keywordLower)) {
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// Check for strong partial matches (keyword at word boundary)
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const wordBoundaryMatch = term.split(/\W+/).some(word => word === keywordLower);
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if (wordBoundaryMatch) {
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score += 2.0; // Strong partial match
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matches.push(`strong_partial:${keyword}`);
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}
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else {
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score += 1.0; // Weak partial match
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matches.push(`partial:${keyword}`);
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}
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}
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}
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});
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});
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// Boost score for domain-specific semantic clusters
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const clusterTerms = this.semanticClusters.get(domain) || [];
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clusterTerms.forEach(clusterTerm => {
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allTerms.forEach(term => {
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if (term.includes(clusterTerm.toLowerCase())) {
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score += 1.2; // Increased from 0.8 to give semantic clusters more weight
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matches.push(`cluster:${clusterTerm}`);
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}
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});
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});
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// Additional scoring for exact domain name matches
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if (queryLower.includes(domain.toLowerCase()) || allTerms.some(term => term === domain.toLowerCase())) {
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score += 3.0;
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matches.push(`domain_name:${domain}`);
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}
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console.log(`DEBUG: Domain ${domain} - Score: ${score}, Matches: ${matches.join(', ')}`);
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if (score > 0) {
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detectedDomains.push({ domain, score });
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}
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}
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console.log('DEBUG: Detected domains:', detectedDomains);
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// Sort by score and extract domain names
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const sortedDomains = detectedDomains
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.sort((a, b) => b.score - a.score)
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.map(d => d.domain);
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// Default to art domain for creative queries if no specific domain detected
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if (sortedDomains.length === 0) {
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// Check if this might be a creative query
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const creativeIndicators = ['painting', 'art', 'music', 'creative', 'aesthetic', 'visual', 'narrative', 'story'];
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const hasCreativeIndicators = allTerms.some(term => creativeIndicators.some(indicator => term.includes(indicator.toLowerCase())));
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if (hasCreativeIndicators) {
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// Try to determine specific creative domain
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if (allTerms.some(term => ['painting', 'visual', 'art', 'aesthetic', 'color', 'canvas'].some(art => term.includes(art)))) {
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sortedDomains.push('art');
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console.log('DEBUG: No specific domains detected but creative visual indicators found, defaulting to art');
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}
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else if (allTerms.some(term => ['music', 'sound', 'rhythm', 'melody', 'harmony'].some(music => term.includes(music)))) {
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sortedDomains.push('music');
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console.log('DEBUG: No specific domains detected but musical indicators found, defaulting to music');
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}
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else if (allTerms.some(term => ['story', 'narrative', 'character', 'plot'].some(narrative => term.includes(narrative)))) {
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sortedDomains.push('narrative');
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console.log('DEBUG: No specific domains detected but narrative indicators found, defaulting to narrative');
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}
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else {
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sortedDomains.push('creative_synthesis');
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console.log('DEBUG: Creative indicators found but no specific domain, defaulting to creative_synthesis');
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}
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}
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else {
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sortedDomains.push('creative_synthesis');
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console.log('DEBUG: No domains detected and no creative indicators, defaulting to creative_synthesis');
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}
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}
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const primaryDomain = sortedDomains[0];
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const reasoningStyle = this.domainPatterns.get(primaryDomain)?.reasoning_style || 'creative_synthesis';
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return {
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domains: sortedDomains.slice(0, 3), // Limit to top 3 domains
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primary_domain: primaryDomain,
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reasoning_style: reasoningStyle,
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cross_domain: sortedDomains.length > 1,
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adaptation_strategy: sortedDomains.length > 1 ? 'multi_domain_synthesis' : 'single_domain_focus',
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detection_scores: detectedDomains.filter(d => d.score > 0),
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debug_info: {
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query_lower: queryLower,
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all_terms: allTerms,
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available_domains: Array.from(this.domainPatterns.keys()),
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all_detection_results: detectedDomains,
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raw_domain_patterns: Object.fromEntries(this.domainPatterns)
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}
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};
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}
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getReasoningGuidance(domains) {
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const guidance = [];
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domains.forEach(domain => {
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const pattern = this.domainPatterns.get(domain);
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if (pattern) {
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guidance.push(this.reasoningStyles.get(pattern.reasoning_style) || 'Apply systematic analysis');
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// Add cross-domain connections
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const crossDomain = this.crossDomainMappings.get(domain);
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if (crossDomain) {
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guidance.push(`Consider ${domain} patterns: ${crossDomain.join(', ')}`);
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}
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}
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});
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return guidance;
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}
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}
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// 2. Creative Reasoning Engine - Generate novel connections for unknown concepts
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class CreativeReasoningEngine {
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analogyPatterns = new Map();
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conceptBridges = new Map();
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emergentPrinciples = [];
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constructor() {
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this.initializeAnalogies();
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this.initializeConceptBridges();
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this.initializeEmergentPrinciples();
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}
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initializeAnalogies() {
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this.analogyPatterns.set('flow', ['current', 'stream', 'river', 'traffic', 'information', 'energy']);
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this.analogyPatterns.set('network', ['web', 'grid', 'mesh', 'connections', 'graph', 'neural']);
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this.analogyPatterns.set('resonance', ['harmony', 'frequency', 'synchronization', 'echo', 'vibration']);
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this.analogyPatterns.set('emergence', ['evolution', 'development', 'growth', 'formation', 'crystallization']);
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this.analogyPatterns.set('quantum', ['probabilistic', 'superposition', 'entangled', 'non-local', 'coherent']);
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this.analogyPatterns.set('consciousness', ['awareness', 'experience', 'integration', 'unified', 'subjective']);
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}
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initializeConceptBridges() {
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this.conceptBridges.set('quantum_consciousness', ['information_integration', 'coherent_states', 'measurement_problem']);
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this.conceptBridges.set('neural_networks', ['distributed_processing', 'adaptive_learning', 'emergent_behavior']);
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this.conceptBridges.set('temporal_dynamics', ['causal_flows', 'evolutionary_processes', 'dynamic_systems']);
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}
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initializeEmergentPrinciples() {
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this.emergentPrinciples = [
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'Information creates structure through selective constraints',
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'Complexity emerges at phase transitions between order and chaos',
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'Consciousness arises from integrated information processing',
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'Temporal dynamics create causal efficacy in complex systems',
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'Resonance patterns enable cross-scale synchronization',
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'Networks exhibit emergent intelligence through connectivity'
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];
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}
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generateCreativeConnections(concepts, context) {
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const connections = [];
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const analogies = [];
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const bridgeConnections = [];
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// Generate analogical connections
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concepts.forEach(concept => {
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const conceptAnalogies = this.findAnalogies(concept);
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conceptAnalogies.forEach(analogy => {
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analogies.push({
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source: concept,
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target: analogy,
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type: 'analogical',
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confidence: 0.7
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});
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connections.push(`${concept} exhibits ${analogy}-like properties`);
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});
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});
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// Generate cross-concept bridges
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for (let i = 0; i < concepts.length; i++) {
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for (let j = i + 1; j < concepts.length; j++) {
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const bridge = this.bridgeConcepts(concepts[i], concepts[j]);
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if (bridge) {
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bridgeConnections.push(bridge);
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connections.push(bridge);
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}
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}
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}
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// Apply emergent principles
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if (concepts.length >= 2) {
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const emergentConnections = this.applyEmergentPrinciples(concepts);
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connections.push(...emergentConnections);
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}
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return {
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creative_connections: connections,
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analogies,
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bridges: bridgeConnections,
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emergent_principles_applied: concepts.length >= 2 ? 2 : 0,
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confidence: connections.length > 0 ? 0.75 : 0.4
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};
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}
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findAnalogies(concept) {
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const analogies = [];
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const conceptLower = concept.toLowerCase();
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// Direct pattern matching
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for (const [pattern, analogs] of this.analogyPatterns) {
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if (conceptLower.includes(pattern)) {
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analogies.push(...analogs);
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}
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}
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// Morphological analogies
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if (conceptLower.endsWith('ium'))
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analogies.push('crystalline', 'resonant', 'conductive');
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if (conceptLower.includes('quantum'))
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analogies.push('probabilistic', 'non-local', 'coherent');
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if (conceptLower.includes('neural'))
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analogies.push('networked', 'adaptive', 'learning');
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if (conceptLower.includes('temporal'))
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analogies.push('dynamic', 'evolutionary', 'causal');
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// Domain-specific analogies
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if (conceptLower.includes('matrix'))
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analogies.push('structured', 'linear', 'computational', 'mathematical');
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if (conceptLower.includes('trading'))
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analogies.push('financial', 'economic', 'strategic', 'algorithmic');
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if (conceptLower.includes('portfolio'))
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analogies.push('diversified', 'balanced', 'optimized', 'financial');
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if (conceptLower.includes('optimization'))
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analogies.push('mathematical', 'algorithmic', 'efficient', 'optimal');
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// Semantic analogies for novel concepts
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if (analogies.length === 0) {
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analogies.push('emergent', 'complex', 'adaptive', 'resonant', 'connected');
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}
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return [...new Set(analogies)];
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}
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bridgeConcepts(concept1, concept2) {
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const bridges = [
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`${concept1} and ${concept2} share information-theoretic foundations`,
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`${concept1} influences ${concept2} through resonance coupling mechanisms`,
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`${concept1} and ${concept2} exhibit complementary aspects of emergence`,
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`${concept1} provides the structure for ${concept2} to manifest dynamics`,
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`${concept1} and ${concept2} co-evolve through mutual information exchange`
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];
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return bridges[Math.floor(Math.random() * bridges.length)];
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}
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applyEmergentPrinciples(concepts) {
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const applications = [];
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const conceptStr = concepts.join(' + ');
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applications.push(`${conceptStr} system exhibits emergent properties beyond individual components`);
|
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applications.push(`${conceptStr} integration creates novel information patterns`);
|
|
applications.push(`${conceptStr} coupling generates higher-order organizational structures`);
|
|
return applications;
|
|
}
|
|
}
|
|
// 3. Enhanced Knowledge Base - Semantic search with analogy linking
|
|
class EnhancedSemanticKnowledgeBase {
|
|
triples = new Map();
|
|
conceptIndex = new Map();
|
|
domainIndex = new Map();
|
|
analogyIndex = new Map();
|
|
semanticClusters = new Map();
|
|
learningEvents = [];
|
|
constructor() {
|
|
this.initializeEnhancedKnowledge();
|
|
}
|
|
initializeEnhancedKnowledge() {
|
|
// Enhanced foundational knowledge with semantic metadata
|
|
this.addSemanticTriple('consciousness', 'emerges_from', 'neural_networks', 0.85, {
|
|
domain_tags: ['consciousness', 'biology', 'computer_science'],
|
|
analogy_links: ['emergence', 'network', 'information_integration'],
|
|
learning_source: 'foundational'
|
|
});
|
|
this.addSemanticTriple('consciousness', 'requires', 'integration', 0.9, {
|
|
domain_tags: ['consciousness', 'physics'],
|
|
analogy_links: ['unity', 'coherence', 'synthesis'],
|
|
learning_source: 'foundational'
|
|
});
|
|
this.addSemanticTriple('quantum_entanglement', 'exhibits', 'non_local_correlation', 0.95, {
|
|
domain_tags: ['physics', 'quantum'],
|
|
analogy_links: ['synchronization', 'connection', 'resonance'],
|
|
learning_source: 'foundational'
|
|
});
|
|
this.addSemanticTriple('neural_networks', 'implement', 'distributed_processing', 1.0, {
|
|
domain_tags: ['computer_science', 'biology'],
|
|
analogy_links: ['parallel', 'collective', 'emergent'],
|
|
learning_source: 'foundational'
|
|
});
|
|
this.addSemanticTriple('temporal_resonance', 'creates', 'causal_efficacy', 0.8, {
|
|
domain_tags: ['temporal', 'physics'],
|
|
analogy_links: ['rhythm', 'synchronization', 'influence'],
|
|
learning_source: 'foundational'
|
|
});
|
|
// Creative domain foundational knowledge
|
|
this.addSemanticTriple('art', 'expresses', 'visual_language', 0.9, {
|
|
domain_tags: ['art', 'communication'],
|
|
analogy_links: ['expression', 'meaning', 'symbolism'],
|
|
learning_source: 'foundational'
|
|
});
|
|
this.addSemanticTriple('pollock_drip_painting', 'demonstrates', 'controlled_chaos', 0.85, {
|
|
domain_tags: ['art', 'physics'],
|
|
analogy_links: ['emergence', 'pattern', 'complexity'],
|
|
learning_source: 'foundational'
|
|
});
|
|
this.addSemanticTriple('music', 'creates', 'harmonic_resonance', 0.9, {
|
|
domain_tags: ['music', 'physics'],
|
|
analogy_links: ['frequency', 'vibration', 'wave'],
|
|
learning_source: 'foundational'
|
|
});
|
|
this.addSemanticTriple('rhythm', 'establishes', 'temporal_pattern', 0.88, {
|
|
domain_tags: ['music', 'temporal'],
|
|
analogy_links: ['periodicity', 'cycle', 'structure'],
|
|
learning_source: 'foundational'
|
|
});
|
|
this.addSemanticTriple('narrative', 'constructs', 'meaning_framework', 0.9, {
|
|
domain_tags: ['narrative', 'philosophy'],
|
|
analogy_links: ['structure', 'coherence', 'understanding'],
|
|
learning_source: 'foundational'
|
|
});
|
|
this.addSemanticTriple('character_development', 'reflects', 'psychological_growth', 0.85, {
|
|
domain_tags: ['narrative', 'psychology'],
|
|
analogy_links: ['evolution', 'change', 'transformation'],
|
|
learning_source: 'foundational'
|
|
});
|
|
this.addSemanticTriple('aesthetic_beauty', 'emerges_from', 'mathematical_proportion', 0.8, {
|
|
domain_tags: ['art', 'mathematics'],
|
|
analogy_links: ['golden_ratio', 'symmetry', 'harmony'],
|
|
learning_source: 'foundational'
|
|
});
|
|
this.addSemanticTriple('emotion', 'influences', 'creative_expression', 0.9, {
|
|
domain_tags: ['emotion', 'art'],
|
|
analogy_links: ['inspiration', 'energy', 'motivation'],
|
|
learning_source: 'foundational'
|
|
});
|
|
this.addSemanticTriple('philosophical_inquiry', 'seeks', 'fundamental_truth', 0.9, {
|
|
domain_tags: ['philosophy', 'consciousness'],
|
|
analogy_links: ['questioning', 'understanding', 'knowledge'],
|
|
learning_source: 'foundational'
|
|
});
|
|
}
|
|
addSemanticTriple(subject, predicate, object, confidence, metadata = {}) {
|
|
const id = crypto.createHash('md5').update(`${subject}_${predicate}_${object}`).digest('hex').substring(0, 16);
|
|
const triple = {
|
|
subject,
|
|
predicate,
|
|
object,
|
|
confidence,
|
|
metadata,
|
|
timestamp: Date.now(),
|
|
usage_count: 0,
|
|
learning_source: metadata.learning_source || 'user_input',
|
|
domain_tags: metadata.domain_tags || [],
|
|
analogy_links: metadata.analogy_links || [],
|
|
related_concepts: this.findSemanticallySimilar(subject, object)
|
|
};
|
|
this.triples.set(id, triple);
|
|
this.updateAllIndices(id, triple);
|
|
return { id, status: 'added', triple };
|
|
}
|
|
findSemanticallySimilar(subject, object) {
|
|
const similar = [];
|
|
[subject, object].forEach(concept => {
|
|
for (const [cluster, terms] of this.semanticClusters) {
|
|
if (concept.toLowerCase().includes(cluster) || terms.some(term => concept.toLowerCase().includes(term))) {
|
|
similar.push(...terms);
|
|
}
|
|
}
|
|
});
|
|
return [...new Set(similar)].filter(s => s !== subject && s !== object);
|
|
}
|
|
updateAllIndices(id, triple) {
|
|
// Concept index
|
|
[triple.subject, triple.object].forEach(concept => {
|
|
if (!this.conceptIndex.has(concept))
|
|
this.conceptIndex.set(concept, new Set());
|
|
this.conceptIndex.get(concept).add(id);
|
|
});
|
|
// Domain index
|
|
if (triple.domain_tags) {
|
|
triple.domain_tags.forEach(domain => {
|
|
if (!this.domainIndex.has(domain))
|
|
this.domainIndex.set(domain, new Set());
|
|
this.domainIndex.get(domain).add(id);
|
|
});
|
|
}
|
|
// Analogy index
|
|
if (triple.analogy_links) {
|
|
triple.analogy_links.forEach(analogy => {
|
|
if (!this.analogyIndex.has(analogy))
|
|
this.analogyIndex.set(analogy, new Set());
|
|
this.analogyIndex.get(analogy).add(id);
|
|
});
|
|
}
|
|
}
|
|
advancedSemanticSearch(query, options = {}) {
|
|
const results = [];
|
|
const queryLower = query.toLowerCase();
|
|
const queryTerms = queryLower.split(/\s+/);
|
|
for (const [id, triple] of this.triples) {
|
|
let relevance = 0;
|
|
// Direct text matching (highest weight)
|
|
if (triple.subject.toLowerCase().includes(queryLower))
|
|
relevance += 3.0;
|
|
if (triple.object.toLowerCase().includes(queryLower))
|
|
relevance += 3.0;
|
|
if (triple.predicate.toLowerCase().includes(queryLower))
|
|
relevance += 2.0;
|
|
// Term-based matching
|
|
queryTerms.forEach(term => {
|
|
if (term.length > 2) {
|
|
if (triple.subject.toLowerCase().includes(term))
|
|
relevance += 1.5;
|
|
if (triple.object.toLowerCase().includes(term))
|
|
relevance += 1.5;
|
|
if (triple.predicate.toLowerCase().includes(term))
|
|
relevance += 0.8;
|
|
}
|
|
});
|
|
// Semantic similarity matching
|
|
if (triple.related_concepts) {
|
|
triple.related_concepts.forEach(concept => {
|
|
if (queryLower.includes(concept.toLowerCase()))
|
|
relevance += 0.6;
|
|
});
|
|
}
|
|
// Analogy-based matching
|
|
if (triple.analogy_links) {
|
|
triple.analogy_links.forEach(analogy => {
|
|
if (queryLower.includes(analogy.toLowerCase()))
|
|
relevance += 0.8;
|
|
});
|
|
}
|
|
// Domain relevance
|
|
if (options.domains && triple.domain_tags) {
|
|
const domainOverlap = triple.domain_tags.filter(d => options.domains.includes(d));
|
|
relevance += domainOverlap.length * 0.5;
|
|
}
|
|
// Usage-based learning boost
|
|
relevance += Math.log(triple.usage_count + 1) * 0.2;
|
|
// Confidence weighting
|
|
relevance *= triple.confidence;
|
|
if (relevance > 0.1) {
|
|
results.push({
|
|
...triple,
|
|
relevance,
|
|
id
|
|
});
|
|
}
|
|
}
|
|
return results
|
|
.sort((a, b) => b.relevance - a.relevance)
|
|
.slice(0, options.limit || 15);
|
|
}
|
|
getAllTriples() {
|
|
return Array.from(this.triples.values());
|
|
}
|
|
markTripleUsed(tripleId) {
|
|
const triple = this.triples.get(tripleId);
|
|
if (triple) {
|
|
triple.usage_count++;
|
|
}
|
|
}
|
|
findCrossDomainConnections(concept, domains) {
|
|
const connections = [];
|
|
domains.forEach(domain => {
|
|
const domainTriples = this.domainIndex.get(domain);
|
|
if (domainTriples) {
|
|
domainTriples.forEach(tripleId => {
|
|
const triple = this.triples.get(tripleId);
|
|
if (triple && (triple.subject.toLowerCase().includes(concept.toLowerCase()) ||
|
|
triple.object.toLowerCase().includes(concept.toLowerCase()))) {
|
|
connections.push(triple);
|
|
}
|
|
});
|
|
}
|
|
});
|
|
return connections;
|
|
}
|
|
recordLearningEvent(event) {
|
|
this.learningEvents.push(event);
|
|
// Auto-generate knowledge from successful patterns
|
|
if (event.confidence > 0.8 && event.concepts.length >= 2) {
|
|
this.generateKnowledgeFromEvent(event);
|
|
}
|
|
// Maintain event history
|
|
if (this.learningEvents.length > 1000) {
|
|
this.learningEvents = this.learningEvents.slice(-1000);
|
|
}
|
|
}
|
|
generateKnowledgeFromEvent(event) {
|
|
for (let i = 0; i < event.concepts.length - 1; i++) {
|
|
const subject = event.concepts[i];
|
|
const object = event.concepts[i + 1];
|
|
let predicate = 'relates_to';
|
|
if (event.tool === 'consciousness')
|
|
predicate = 'influences_consciousness';
|
|
if (event.tool === 'neural')
|
|
predicate = 'processes_through';
|
|
if (event.analogies && event.analogies.length > 0)
|
|
predicate = 'analogous_to';
|
|
this.addSemanticTriple(subject, predicate, object, event.confidence * 0.8, {
|
|
domain_tags: event.domains || ['learned'],
|
|
analogy_links: event.analogies || [],
|
|
learning_source: `${event.tool}_interaction`,
|
|
type: 'auto_generated'
|
|
});
|
|
}
|
|
}
|
|
}
|
|
// 4. Analogical Reasoning - Cross-domain concept bridging
|
|
class AnalogicalReasoningEngine {
|
|
analogyMappings = new Map();
|
|
crossDomainBridges = new Map();
|
|
structuralMappings = new Map();
|
|
constructor() {
|
|
this.initializeAnalogicalMappings();
|
|
this.initializeCrossDomainBridges();
|
|
this.initializeStructuralMappings();
|
|
}
|
|
initializeAnalogicalMappings() {
|
|
this.analogyMappings.set('quantum_consciousness', {
|
|
source_domain: 'quantum_mechanics',
|
|
target_domain: 'consciousness',
|
|
mappings: {
|
|
'superposition': 'multiple_states_of_awareness',
|
|
'entanglement': 'unified_conscious_experience',
|
|
'measurement': 'subjective_observation',
|
|
'coherence': 'integrated_consciousness'
|
|
}
|
|
});
|
|
this.analogyMappings.set('neural_network', {
|
|
source_domain: 'brain_biology',
|
|
target_domain: 'artificial_intelligence',
|
|
mappings: {
|
|
'neurons': 'processing_nodes',
|
|
'synapses': 'weighted_connections',
|
|
'plasticity': 'adaptive_learning',
|
|
'networks': 'computational_graphs'
|
|
}
|
|
});
|
|
this.analogyMappings.set('temporal_flow', {
|
|
source_domain: 'physics',
|
|
target_domain: 'information_processing',
|
|
mappings: {
|
|
'time_flow': 'information_propagation',
|
|
'causality': 'computational_dependencies',
|
|
'temporal_order': 'sequential_processing',
|
|
'synchronization': 'coordinated_operations'
|
|
}
|
|
});
|
|
}
|
|
initializeCrossDomainBridges() {
|
|
this.crossDomainBridges.set('physics_consciousness', [
|
|
'information_integration_principles',
|
|
'field_effects_and_awareness',
|
|
'quantum_coherence_and_unity'
|
|
]);
|
|
this.crossDomainBridges.set('biology_computing', [
|
|
'adaptive_algorithms',
|
|
'evolutionary_optimization',
|
|
'distributed_intelligence'
|
|
]);
|
|
this.crossDomainBridges.set('temporal_consciousness', [
|
|
'temporal_binding_of_experience',
|
|
'causal_efficacy_of_awareness',
|
|
'time_dependent_integration'
|
|
]);
|
|
}
|
|
initializeStructuralMappings() {
|
|
this.structuralMappings.set('resonance_systems', {
|
|
structure: 'oscillatory_coupling',
|
|
elements: ['frequency', 'amplitude', 'phase', 'synchronization'],
|
|
relations: ['resonant_coupling', 'harmonic_interaction', 'phase_locking']
|
|
});
|
|
this.structuralMappings.set('network_systems', {
|
|
structure: 'graph_connectivity',
|
|
elements: ['nodes', 'edges', 'clusters', 'paths'],
|
|
relations: ['connectivity', 'information_flow', 'emergent_behavior']
|
|
});
|
|
}
|
|
performAnalogicalReasoning(concepts, domains) {
|
|
const analogies = [];
|
|
const bridges = [];
|
|
const structuralMaps = [];
|
|
// Find direct analogical mappings
|
|
concepts.forEach(concept => {
|
|
for (const [key, mapping] of this.analogyMappings) {
|
|
if (concept.toLowerCase().includes(key.split('_')[0])) {
|
|
analogies.push({
|
|
concept,
|
|
analogy_type: key,
|
|
source_domain: mapping.source_domain,
|
|
target_domain: mapping.target_domain,
|
|
mappings: mapping.mappings,
|
|
confidence: 0.8
|
|
});
|
|
}
|
|
}
|
|
});
|
|
// Generate cross-domain bridges
|
|
if (domains.length > 1) {
|
|
for (let i = 0; i < domains.length; i++) {
|
|
for (let j = i + 1; j < domains.length; j++) {
|
|
const bridgeKey = `${domains[i]}_${domains[j]}`;
|
|
const reverseBridgeKey = `${domains[j]}_${domains[i]}`;
|
|
const bridgeData = this.crossDomainBridges.get(bridgeKey) ||
|
|
this.crossDomainBridges.get(reverseBridgeKey);
|
|
if (bridgeData) {
|
|
bridges.push(...bridgeData);
|
|
}
|
|
else {
|
|
// Generate novel cross-domain bridge
|
|
bridges.push(`${domains[i]} principles may inform ${domains[j]} understanding`);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
// Apply structural mappings
|
|
concepts.forEach(concept => {
|
|
for (const [key, structure] of this.structuralMappings) {
|
|
if (concept.toLowerCase().includes(key.split('_')[0])) {
|
|
structuralMaps.push({
|
|
concept,
|
|
structure_type: key,
|
|
structure: structure.structure,
|
|
elements: structure.elements,
|
|
relations: structure.relations
|
|
});
|
|
}
|
|
}
|
|
});
|
|
return {
|
|
analogies,
|
|
cross_domain_bridges: bridges,
|
|
structural_mappings: structuralMaps,
|
|
confidence: analogies.length > 0 ? 0.85 : 0.6
|
|
};
|
|
}
|
|
generateNovelAnalogies(unknownConcept, knownDomains) {
|
|
const novelAnalogies = [];
|
|
// Generate analogies based on morphological structure
|
|
const conceptLower = unknownConcept.toLowerCase();
|
|
if (conceptLower.includes('quantum')) {
|
|
novelAnalogies.push({
|
|
source: unknownConcept,
|
|
target: 'probabilistic_system',
|
|
basis: 'quantum_behavior_patterns',
|
|
confidence: 0.7
|
|
});
|
|
}
|
|
if (conceptLower.includes('neural') || conceptLower.includes('network')) {
|
|
novelAnalogies.push({
|
|
source: unknownConcept,
|
|
target: 'distributed_processing_system',
|
|
basis: 'network_connectivity_patterns',
|
|
confidence: 0.75
|
|
});
|
|
}
|
|
if (conceptLower.includes('temporal') || conceptLower.includes('time')) {
|
|
novelAnalogies.push({
|
|
source: unknownConcept,
|
|
target: 'dynamic_flow_system',
|
|
basis: 'temporal_evolution_patterns',
|
|
confidence: 0.7
|
|
});
|
|
}
|
|
// Generate based on known domain principles
|
|
knownDomains.forEach(domain => {
|
|
novelAnalogies.push({
|
|
source: unknownConcept,
|
|
target: `${domain}_like_behavior`,
|
|
basis: `structural_similarity_to_${domain}`,
|
|
confidence: 0.6
|
|
});
|
|
});
|
|
return novelAnalogies;
|
|
}
|
|
}
|
|
// Complete Enhanced Psycho-Symbolic Reasoning Tool with Learning Hooks
|
|
export class PsychoSymbolicTools {
|
|
knowledgeBase;
|
|
domainEngine;
|
|
creativeEngine;
|
|
analogicalEngine;
|
|
performanceCache;
|
|
toolLearningHooks = new Map();
|
|
constructor() {
|
|
this.knowledgeBase = new EnhancedSemanticKnowledgeBase();
|
|
this.domainEngine = new DomainAdaptationEngine();
|
|
this.creativeEngine = new CreativeReasoningEngine();
|
|
this.analogicalEngine = new AnalogicalReasoningEngine();
|
|
this.performanceCache = new ReasoningCache();
|
|
}
|
|
getTools() {
|
|
return [
|
|
{
|
|
name: 'psycho_symbolic_reason',
|
|
description: 'Complete enhanced psycho-symbolic reasoning with domain adaptation, creative synthesis, and analogical reasoning',
|
|
inputSchema: {
|
|
type: 'object',
|
|
properties: {
|
|
query: { type: 'string', description: 'The reasoning query' },
|
|
context: { type: 'object', description: 'Additional context', default: {} },
|
|
depth: { type: 'number', description: 'Maximum reasoning depth', default: 7 },
|
|
use_cache: { type: 'boolean', description: 'Enable intelligent caching', default: true },
|
|
enable_learning: { type: 'boolean', description: 'Enable learning from this interaction', default: true },
|
|
creative_mode: { type: 'boolean', description: 'Enable creative reasoning for novel concepts', default: true },
|
|
domain_adaptation: { type: 'boolean', description: 'Enable automatic domain detection and adaptation', default: true },
|
|
analogical_reasoning: { type: 'boolean', description: 'Enable analogical reasoning across domains', default: true }
|
|
},
|
|
required: ['query']
|
|
}
|
|
},
|
|
{
|
|
name: 'knowledge_graph_query',
|
|
description: 'Advanced semantic knowledge search with analogy linking and domain filtering',
|
|
inputSchema: {
|
|
type: 'object',
|
|
properties: {
|
|
query: { type: 'string', description: 'Natural language query' },
|
|
domains: { type: 'array', description: 'Domain filters', default: [] },
|
|
include_analogies: { type: 'boolean', description: 'Include analogical connections', default: true },
|
|
limit: { type: 'number', description: 'Max results', default: 20 }
|
|
},
|
|
required: ['query']
|
|
}
|
|
},
|
|
{
|
|
name: 'add_knowledge',
|
|
description: 'Add knowledge with full semantic metadata, domain tags, and analogy links',
|
|
inputSchema: {
|
|
type: 'object',
|
|
properties: {
|
|
subject: { type: 'string' },
|
|
predicate: { type: 'string' },
|
|
object: { type: 'string' },
|
|
confidence: { type: 'number', default: 1.0 },
|
|
metadata: {
|
|
type: 'object',
|
|
description: 'Enhanced metadata with domain_tags, analogy_links, etc.',
|
|
default: {}
|
|
}
|
|
},
|
|
required: ['subject', 'predicate', 'object']
|
|
}
|
|
},
|
|
{
|
|
name: 'register_tool_interaction',
|
|
description: 'Register interaction with other tools for cross-tool learning',
|
|
inputSchema: {
|
|
type: 'object',
|
|
properties: {
|
|
tool_name: { type: 'string', description: 'Name of the interacting tool' },
|
|
query: { type: 'string', description: 'Query sent to the tool' },
|
|
result: { type: 'object', description: 'Result from the tool' },
|
|
concepts: { type: 'array', description: 'Concepts involved in the interaction' }
|
|
},
|
|
required: ['tool_name', 'query', 'result', 'concepts']
|
|
}
|
|
},
|
|
{
|
|
name: 'learning_status',
|
|
description: 'Get comprehensive learning system status with cross-tool insights',
|
|
inputSchema: {
|
|
type: 'object',
|
|
properties: {
|
|
detailed: { type: 'boolean', description: 'Include detailed learning metrics', default: false }
|
|
}
|
|
}
|
|
}
|
|
];
|
|
}
|
|
async handleToolCall(name, args) {
|
|
switch (name) {
|
|
case 'psycho_symbolic_reason':
|
|
return this.performCompleteReasoning(args);
|
|
case 'knowledge_graph_query':
|
|
return this.advancedKnowledgeQuery(args);
|
|
case 'add_knowledge':
|
|
return this.addEnhancedKnowledge(args);
|
|
case 'register_tool_interaction':
|
|
return this.registerToolInteraction(args);
|
|
case 'learning_status':
|
|
return this.getLearningStatus(args.detailed || false);
|
|
default:
|
|
throw new Error(`Unknown tool: ${name}`);
|
|
}
|
|
}
|
|
async performCompleteReasoning(args) {
|
|
const startTime = performance.now();
|
|
const { query, context = {}, depth = 7, use_cache = true, enable_learning = true, creative_mode = true, domain_adaptation = true, analogical_reasoning = true } = args;
|
|
// Cache check
|
|
if (use_cache) {
|
|
const cached = this.performanceCache.get(query, context, depth);
|
|
if (cached) {
|
|
return {
|
|
...cached.result,
|
|
cached: true,
|
|
cache_hit: true,
|
|
compute_time: performance.now() - startTime,
|
|
cache_metrics: this.performanceCache.getMetrics()
|
|
};
|
|
}
|
|
}
|
|
const reasoningSteps = [];
|
|
const insights = new Set();
|
|
// Step 1: Enhanced Entity Extraction
|
|
const entities = this.extractAdvancedEntities(query);
|
|
reasoningSteps.push({
|
|
type: 'enhanced_entity_extraction',
|
|
entities: entities.entities,
|
|
concepts: entities.concepts,
|
|
relationships: entities.relationships,
|
|
novel_concepts: entities.novel_concepts,
|
|
confidence: 0.9
|
|
});
|
|
// Step 2: Domain Adaptation
|
|
let domainInfo = { domains: ['general'], reasoning_style: 'exploratory' };
|
|
if (domain_adaptation) {
|
|
domainInfo = this.domainEngine.detectDomains(query, entities.concepts);
|
|
const guidance = this.domainEngine.getReasoningGuidance(domainInfo.domains);
|
|
reasoningSteps.push({
|
|
type: 'domain_adaptation',
|
|
detected_domains: domainInfo.domains,
|
|
reasoning_style: domainInfo.reasoning_style,
|
|
adaptation_strategy: domainInfo.adaptation_strategy,
|
|
reasoning_guidance: guidance,
|
|
confidence: 0.85,
|
|
debug_info: domainInfo.debug_info
|
|
});
|
|
guidance.forEach(g => insights.add(g));
|
|
}
|
|
// Step 3: Creative Reasoning for Novel Concepts
|
|
if (creative_mode && entities.novel_concepts.length > 0) {
|
|
const creativeResults = this.creativeEngine.generateCreativeConnections(entities.novel_concepts, context);
|
|
creativeResults.creative_connections.forEach(conn => insights.add(conn));
|
|
reasoningSteps.push({
|
|
type: 'creative_reasoning',
|
|
novel_concepts: entities.novel_concepts,
|
|
creative_connections: creativeResults.creative_connections,
|
|
analogies: creativeResults.analogies,
|
|
bridges: creativeResults.bridges,
|
|
confidence: creativeResults.confidence
|
|
});
|
|
}
|
|
// Step 4: Enhanced Knowledge Traversal
|
|
const knowledgeResults = await this.enhancedKnowledgeTraversal(entities.concepts, domainInfo.domains);
|
|
knowledgeResults.discoveries.forEach(d => insights.add(d));
|
|
reasoningSteps.push({
|
|
type: 'enhanced_knowledge_traversal',
|
|
paths: knowledgeResults.paths,
|
|
discoveries: knowledgeResults.discoveries,
|
|
cross_domain_connections: knowledgeResults.cross_domain_connections,
|
|
confidence: knowledgeResults.confidence
|
|
});
|
|
// Step 5: Analogical Reasoning
|
|
if (analogical_reasoning) {
|
|
const analogicalResults = this.analogicalEngine.performAnalogicalReasoning(entities.concepts, domainInfo.domains);
|
|
reasoningSteps.push({
|
|
type: 'analogical_reasoning',
|
|
analogies: analogicalResults.analogies,
|
|
cross_domain_bridges: analogicalResults.cross_domain_bridges,
|
|
structural_mappings: analogicalResults.structural_mappings,
|
|
confidence: analogicalResults.confidence
|
|
});
|
|
analogicalResults.cross_domain_bridges.forEach(bridge => insights.add(bridge));
|
|
// Generate novel analogies for unknown concepts
|
|
if (entities.novel_concepts.length > 0) {
|
|
const novelAnalogies = this.analogicalEngine.generateNovelAnalogies(entities.novel_concepts[0], domainInfo.domains);
|
|
reasoningSteps.push({
|
|
type: 'novel_analogical_reasoning',
|
|
novel_analogies: novelAnalogies,
|
|
confidence: 0.7
|
|
});
|
|
}
|
|
}
|
|
// Step 6: Cross-Tool Learning Integration
|
|
const toolInsights = this.getCrossToolInsights(entities.concepts);
|
|
if (toolInsights.length > 0) {
|
|
toolInsights.forEach(insight => insights.add(insight));
|
|
reasoningSteps.push({
|
|
type: 'cross_tool_learning',
|
|
tool_insights: toolInsights,
|
|
confidence: 0.8
|
|
});
|
|
}
|
|
// Step 7: Advanced Synthesis
|
|
const synthesis = this.synthesizeAdvancedAnswer(query, Array.from(insights), reasoningSteps, domainInfo, entities);
|
|
// Record learning event
|
|
if (enable_learning) {
|
|
this.knowledgeBase.recordLearningEvent({
|
|
tool: 'complete_psycho_symbolic_reasoner',
|
|
action: 'comprehensive_reasoning',
|
|
concepts: entities.concepts,
|
|
patterns: [domainInfo.reasoning_style],
|
|
outcome: synthesis.answer,
|
|
timestamp: Date.now(),
|
|
confidence: synthesis.confidence,
|
|
domains: domainInfo.domains,
|
|
analogies: reasoningSteps.find(s => s.type === 'analogical_reasoning')?.analogies?.map((a) => a.concept) || []
|
|
});
|
|
}
|
|
const result = {
|
|
answer: synthesis.answer,
|
|
confidence: synthesis.confidence,
|
|
reasoning: reasoningSteps,
|
|
insights: Array.from(insights),
|
|
detected_domains: domainInfo.domains,
|
|
reasoning_style: domainInfo.reasoning_style,
|
|
depth: depth,
|
|
entities: entities.entities,
|
|
concepts: entities.concepts,
|
|
novel_concepts: entities.novel_concepts,
|
|
triples_examined: knowledgeResults.triples_examined,
|
|
creative_connections: creative_mode ? reasoningSteps.find(s => s.type === 'creative_reasoning')?.creative_connections?.length || 0 : 0,
|
|
analogies_explored: analogical_reasoning ? reasoningSteps.find(s => s.type === 'analogical_reasoning')?.analogies?.length || 0 : 0,
|
|
cross_tool_insights: toolInsights.length
|
|
};
|
|
// Cache result
|
|
if (use_cache) {
|
|
this.performanceCache.set(query, context, depth, result, performance.now() - startTime);
|
|
}
|
|
return {
|
|
...result,
|
|
cached: false,
|
|
cache_hit: false,
|
|
compute_time: performance.now() - startTime,
|
|
cache_metrics: use_cache ? this.performanceCache.getMetrics() : null
|
|
};
|
|
}
|
|
extractAdvancedEntities(query) {
|
|
const words = query.split(/\s+/);
|
|
const entities = [];
|
|
const concepts = [];
|
|
const relationships = [];
|
|
const novel_concepts = [];
|
|
// Enhanced concept extraction with domain awareness
|
|
const domainTerms = [
|
|
'consciousness', 'neural', 'quantum', 'temporal', 'resonance', 'emergence',
|
|
'integration', 'plasticity', 'learning', 'information', 'complexity',
|
|
'synchronization', 'coherence', 'entanglement', 'superposition'
|
|
];
|
|
const commonWords = new Set([
|
|
'the', 'and', 'or', 'but', 'for', 'with', 'from', 'what', 'how', 'why',
|
|
'when', 'where', 'does', 'can', 'will', 'would', 'could', 'should'
|
|
]);
|
|
words.forEach(word => {
|
|
const wordLower = word.toLowerCase();
|
|
if (word.length > 3 && !commonWords.has(wordLower)) {
|
|
concepts.push(wordLower);
|
|
// Check if it's a known domain term
|
|
if (!domainTerms.some(term => wordLower.includes(term)) &&
|
|
!this.knowledgeBase.getAllTriples().some(t => t.subject.toLowerCase().includes(wordLower) ||
|
|
t.object.toLowerCase().includes(wordLower))) {
|
|
novel_concepts.push(wordLower);
|
|
}
|
|
}
|
|
// Extract named entities
|
|
if (/^[A-Z]/.test(word) && word.length > 2) {
|
|
entities.push(wordLower);
|
|
}
|
|
});
|
|
// Extract relationships
|
|
const relationshipPatterns = [
|
|
'relate', 'connect', 'influence', 'create', 'emerge', 'exhibit',
|
|
'require', 'enable', 'cause', 'affect', 'bridge', 'synchronize'
|
|
];
|
|
relationshipPatterns.forEach(pattern => {
|
|
if (query.toLowerCase().includes(pattern)) {
|
|
relationships.push(pattern);
|
|
}
|
|
});
|
|
return {
|
|
entities: [...new Set(entities)],
|
|
concepts: [...new Set(concepts)],
|
|
relationships: [...new Set(relationships)],
|
|
novel_concepts: [...new Set(novel_concepts)]
|
|
};
|
|
}
|
|
async enhancedKnowledgeTraversal(concepts, domains) {
|
|
const paths = [];
|
|
const discoveries = [];
|
|
const cross_domain_connections = [];
|
|
let triples_examined = 0;
|
|
for (const concept of concepts) {
|
|
const results = this.knowledgeBase.advancedSemanticSearch(concept, { domains, limit: 15 });
|
|
triples_examined += results.length;
|
|
results.forEach(result => {
|
|
this.knowledgeBase.markTripleUsed(result.id);
|
|
discoveries.push(`${result.subject} ${result.predicate} ${result.object}`);
|
|
paths.push([result.subject, result.object]);
|
|
});
|
|
// Find cross-domain connections
|
|
if (domains.length > 0) {
|
|
const crossDomain = this.knowledgeBase.findCrossDomainConnections(concept, domains);
|
|
cross_domain_connections.push(...crossDomain);
|
|
}
|
|
}
|
|
return {
|
|
paths,
|
|
discoveries,
|
|
cross_domain_connections,
|
|
confidence: discoveries.length > 0 ? 0.9 : 0.4,
|
|
triples_examined
|
|
};
|
|
}
|
|
synthesizeAdvancedAnswer(query, insights, reasoningSteps, domainInfo, entities) {
|
|
let answer = '';
|
|
let confidence = 0.8;
|
|
const hasNovelConcepts = entities.novel_concepts.length > 0;
|
|
const isMultiDomain = domainInfo.domains.length > 1;
|
|
const hasCreativeConnections = reasoningSteps.some(s => s.type === 'creative_reasoning');
|
|
const hasAnalogies = reasoningSteps.some(s => s.type === 'analogical_reasoning');
|
|
if (insights.length === 0) {
|
|
answer = `This query explores novel conceptual territory that transcends conventional knowledge boundaries. Through ${domainInfo.reasoning_style} analysis, emergent patterns suggest interdisciplinary synthesis opportunities.`;
|
|
confidence = 0.65;
|
|
}
|
|
else if (hasNovelConcepts && hasCreativeConnections) {
|
|
answer = `Through creative synthesis across ${domainInfo.domains.join(' and ')} domains: ${insights.slice(0, 4).join('. ')}.`;
|
|
confidence = 0.8;
|
|
}
|
|
else if (isMultiDomain && hasAnalogies) {
|
|
answer = `Analogical reasoning reveals: ${insights.slice(0, 5).join('. ')}.`;
|
|
confidence = 0.85;
|
|
}
|
|
else {
|
|
const primaryDomain = domainInfo.domains[0];
|
|
answer = `From a ${primaryDomain} perspective using ${domainInfo.reasoning_style}: ${insights.slice(0, 5).join('. ')}.`;
|
|
confidence = 0.9;
|
|
}
|
|
return { answer, confidence };
|
|
}
|
|
advancedKnowledgeQuery(args) {
|
|
const { query, domains = [], include_analogies = true, limit = 20 } = args;
|
|
const results = this.knowledgeBase.advancedSemanticSearch(query, { domains, limit });
|
|
let analogies = [];
|
|
if (include_analogies) {
|
|
results.forEach(result => {
|
|
if (result.analogy_links) {
|
|
result.analogy_links.forEach((analogy) => {
|
|
analogies.push({
|
|
source: result.subject,
|
|
analogy,
|
|
confidence: result.confidence * 0.8
|
|
});
|
|
});
|
|
}
|
|
});
|
|
}
|
|
return {
|
|
query,
|
|
results: results.map(r => ({
|
|
subject: r.subject,
|
|
predicate: r.predicate,
|
|
object: r.object,
|
|
confidence: r.confidence,
|
|
relevance: r.relevance,
|
|
domain_tags: r.domain_tags,
|
|
analogy_links: r.analogy_links,
|
|
usage_count: r.usage_count,
|
|
learning_source: r.learning_source
|
|
})),
|
|
analogies: include_analogies ? analogies : [],
|
|
domains_searched: domains,
|
|
total: results.length,
|
|
totalAvailable: this.knowledgeBase.getAllTriples().length
|
|
};
|
|
}
|
|
addEnhancedKnowledge(args) {
|
|
const { subject, predicate, object, confidence = 1.0, metadata = {} } = args;
|
|
return this.knowledgeBase.addSemanticTriple(subject, predicate, object, confidence, {
|
|
...metadata,
|
|
learning_source: metadata.learning_source || 'user_input'
|
|
});
|
|
}
|
|
registerToolInteraction(args) {
|
|
const { tool_name, query, result, concepts } = args;
|
|
if (!this.toolLearningHooks.has(tool_name)) {
|
|
this.toolLearningHooks.set(tool_name, []);
|
|
}
|
|
const interaction = {
|
|
tool: tool_name,
|
|
query,
|
|
result,
|
|
concepts,
|
|
timestamp: Date.now(),
|
|
success: result.confidence > 0.7
|
|
};
|
|
this.toolLearningHooks.get(tool_name).push(interaction);
|
|
// Learn from successful interactions
|
|
if (interaction.success) {
|
|
this.knowledgeBase.recordLearningEvent({
|
|
tool: tool_name,
|
|
action: 'external_interaction',
|
|
concepts,
|
|
patterns: result.patterns || [],
|
|
outcome: result.answer || 'success',
|
|
timestamp: Date.now(),
|
|
confidence: result.confidence,
|
|
domains: result.detected_domains || []
|
|
});
|
|
}
|
|
return {
|
|
status: 'registered',
|
|
tool: tool_name,
|
|
learning_active: interaction.success,
|
|
total_interactions: this.toolLearningHooks.get(tool_name).length
|
|
};
|
|
}
|
|
getCrossToolInsights(concepts) {
|
|
const insights = [];
|
|
for (const [tool, interactions] of this.toolLearningHooks) {
|
|
const relevantInteractions = interactions.filter((interaction) => concepts.some(concept => interaction.concepts.includes(concept) ||
|
|
interaction.query.toLowerCase().includes(concept.toLowerCase())));
|
|
if (relevantInteractions.length > 0) {
|
|
insights.push(`${tool} tool has processed ${relevantInteractions.length} similar concept interactions`);
|
|
const successfulInteractions = relevantInteractions.filter((i) => i.success);
|
|
if (successfulInteractions.length > 0) {
|
|
insights.push(`${tool} achieved ${Math.round(successfulInteractions.length / relevantInteractions.length * 100)}% success rate with similar concepts`);
|
|
}
|
|
}
|
|
}
|
|
return insights;
|
|
}
|
|
getLearningStatus(detailed) {
|
|
const totalTriples = this.knowledgeBase.getAllTriples().length;
|
|
const learnedTriples = this.knowledgeBase.getAllTriples().filter(t => t.learning_source !== 'foundational').length;
|
|
const totalToolInteractions = Array.from(this.toolLearningHooks.values()).reduce((sum, interactions) => sum + interactions.length, 0);
|
|
if (detailed) {
|
|
return {
|
|
knowledge_base: {
|
|
total_triples: totalTriples,
|
|
learned_triples: learnedTriples,
|
|
learning_ratio: totalTriples > 0 ? learnedTriples / totalTriples : 0
|
|
},
|
|
cross_tool_learning: {
|
|
registered_tools: this.toolLearningHooks.size,
|
|
total_interactions: totalToolInteractions,
|
|
tools: Array.from(this.toolLearningHooks.keys())
|
|
},
|
|
capabilities: {
|
|
domain_adaptation: true,
|
|
creative_reasoning: true,
|
|
analogical_reasoning: true,
|
|
semantic_search: true,
|
|
cross_tool_integration: true
|
|
},
|
|
cache_metrics: this.performanceCache.getMetrics()
|
|
};
|
|
}
|
|
return {
|
|
learning_active: true,
|
|
total_knowledge: totalTriples,
|
|
learned_concepts: learnedTriples,
|
|
tool_integrations: this.toolLearningHooks.size,
|
|
cross_tool_interactions: totalToolInteractions
|
|
};
|
|
}
|
|
}
|