tasq/node_modules/agentdb/dist/simulation/scenarios/consciousness-explorer.js

108 lines
4.8 KiB
JavaScript

/**
* 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) {
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(), embedder, undefined, undefined, db.getGraphDatabase());
const causal = new CausalMemoryGraph(db.getGraphDatabase(), db.getGraphDatabase());
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;
}
};
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