/** * Consciousness-Explorer Integration * * Multi-layered graph for consciousness models * Integration with consciousness-explorer package * * Explores: * - Global workspace theory * - Integrated information theory * - Higher-order thought models * - Metacognition layers */ import { createUnifiedDatabase } from '../../src/db-unified.js'; import { ReflexionMemory } from '../../src/controllers/ReflexionMemory.js'; import { CausalMemoryGraph } from '../../src/controllers/CausalMemoryGraph.js'; import { EmbeddingService } from '../../src/controllers/EmbeddingService.js'; import * as path from 'path'; export default { description: 'Consciousness-explorer with multi-layered consciousness models', async run(config: any) { const { verbosity = 2, layers = 4 } = config; if (verbosity >= 2) { console.log(` 🌌 Initializing Consciousness Explorer (${layers} layers)`); } // Initialize multi-layered consciousness graph const embedder = new EmbeddingService({ model: 'Xenova/all-MiniLM-L6-v2', dimension: 384, provider: 'transformers' }); await embedder.initialize(); const db = await createUnifiedDatabase( path.join(process.cwd(), 'simulation', 'data', 'advanced', 'consciousness.graph'), embedder, { forceMode: 'graph' } ); const reflexion = new ReflexionMemory( db.getGraphDatabase() as any, embedder, undefined, undefined, db.getGraphDatabase() as any ); const causal = new CausalMemoryGraph( db.getGraphDatabase() as any, db.getGraphDatabase() as any ); const results = { perceptualLayer: 0, attentionLayer: 0, metacognitiveLayer: 0, integratedInformation: 0, consciousnessLevel: 0, totalTime: 0 }; const startTime = performance.now(); // Layer 1: Perceptual Processing const perceptualInputs = ['visual', 'auditory', 'tactile']; for (const input of perceptualInputs) { await reflexion.storeEpisode({ sessionId: 'consciousness-layer-1', task: `perceptual_input: ${input}`, reward: 0.75, success: true, input: `${input}_stimulus`, output: `${input}_percept` }); results.perceptualLayer++; } // Layer 2: Attention & Global Workspace const attentionTargets = ['salient_object', 'motion_pattern', 'unexpected_event']; for (const target of attentionTargets) { await reflexion.storeEpisode({ sessionId: 'consciousness-layer-2', task: `attention_focus: ${target}`, reward: 0.85, success: true, input: 'workspace_broadcast', output: `attended_${target}` }); results.attentionLayer++; } // Layer 3: Metacognitive Monitoring const metacognitiveProcesses = ['self_monitoring', 'error_detection', 'strategy_selection']; for (const process of metacognitiveProcesses) { await reflexion.storeEpisode({ sessionId: 'consciousness-layer-3', task: `metacognition: ${process}`, reward: 0.90, success: true, input: 'cognitive_state', output: `metacognitive_${process}`, critique: 'Self-reflective awareness' }); results.metacognitiveLayer++; } // Integrated Information (phi) // Measure of consciousness based on information integration results.integratedInformation = (results.perceptualLayer + results.attentionLayer + results.metacognitiveLayer) / 3; // Consciousness level (normalized) results.consciousnessLevel = (0.75 * results.perceptualLayer + 0.85 * results.attentionLayer + 0.90 * results.metacognitiveLayer) / (results.perceptualLayer + results.attentionLayer + results.metacognitiveLayer); const endTime = performance.now(); results.totalTime = endTime - startTime; db.close(); if (verbosity >= 2) { console.log(` 📊 Perceptual Layer: ${results.perceptualLayer} processes`); console.log(` 📊 Attention Layer: ${results.attentionLayer} processes`); console.log(` 📊 Metacognitive Layer: ${results.metacognitiveLayer} processes`); console.log(` 📊 Integrated Information (φ): ${results.integratedInformation.toFixed(2)}`); console.log(` 📊 Consciousness Level: ${(results.consciousnessLevel * 100).toFixed(1)}%`); console.log(` ⏱️ Duration: ${results.totalTime.toFixed(2)}ms`); } return results; } };