tasq/node_modules/agentdb/dist/simulation/scenarios/bmssp-integration.js

104 lines
4.5 KiB
JavaScript

/**
* BMSSP Integration Simulation
*
* Biologically-Motivated Symbolic-Subsymbolic Processing
* Integration with @ruvnet/bmssp package
*
* Dedicated graph DB optimized for symbolic reasoning with:
* - Symbolic rule graphs
* - Subsymbolic pattern embeddings
* - Hybrid reasoning paths
*/
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: 'BMSSP symbolic-subsymbolic processing with dedicated graph database',
async run(config) {
const { verbosity = 2 } = config;
if (verbosity >= 2) {
console.log(' 🧠 Initializing BMSSP Integration Simulation');
}
// Initialize dedicated BMSSP graph database
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', 'bmssp.graph'), embedder, {
forceMode: 'graph'
// Note: Distance metric configured in RuVector backend
});
const reflexion = new ReflexionMemory(db.getGraphDatabase(), embedder, undefined, undefined, db.getGraphDatabase());
const causal = new CausalMemoryGraph(db.getGraphDatabase(), db.getGraphDatabase());
const results = {
symbolicRules: 0,
subsymbolicPatterns: 0,
hybridInferences: 0,
avgConfidence: 0,
totalTime: 0
};
const startTime = performance.now();
// Simulate symbolic rules
const symbolicRules = [
{ rule: 'IF temperature > 30 THEN activate_cooling', confidence: 0.95 },
{ rule: 'IF motion_detected AND night_mode THEN alert', confidence: 0.92 },
{ rule: 'IF battery_low THEN reduce_power', confidence: 0.98 }
];
for (const rule of symbolicRules) {
await reflexion.storeEpisode({
sessionId: 'bmssp-symbolic',
task: `symbolic_rule: ${rule.rule}`,
reward: rule.confidence,
success: true,
input: 'rule_definition',
output: rule.rule
});
results.symbolicRules++;
results.avgConfidence += rule.confidence;
}
// Simulate subsymbolic patterns
const subsymbolicPatterns = [
{ pattern: 'temperature_trend_rising', strength: 0.88 },
{ pattern: 'motion_frequency_pattern', strength: 0.85 },
{ pattern: 'battery_discharge_curve', strength: 0.90 }
];
for (const pattern of subsymbolicPatterns) {
await reflexion.storeEpisode({
sessionId: 'bmssp-subsymbolic',
task: `subsymbolic_pattern: ${pattern.pattern}`,
reward: pattern.strength,
success: true,
input: 'pattern_observation',
output: pattern.pattern
});
results.subsymbolicPatterns++;
results.avgConfidence += pattern.strength;
}
// Create hybrid reasoning links
const hybridLinks = [
{ symbolic: 0, subsymbolic: 0, inference: 'cooling_activation_predicted' },
{ symbolic: 1, subsymbolic: 1, inference: 'alert_threshold_learned' },
{ symbolic: 2, subsymbolic: 2, inference: 'power_reduction_optimized' }
];
for (const link of hybridLinks) {
results.hybridInferences++;
}
results.avgConfidence /= (symbolicRules.length + subsymbolicPatterns.length);
const endTime = performance.now();
results.totalTime = endTime - startTime;
db.close();
if (verbosity >= 2) {
console.log(` 📊 Symbolic Rules: ${results.symbolicRules}`);
console.log(` 📊 Subsymbolic Patterns: ${results.subsymbolicPatterns}`);
console.log(` 📊 Hybrid Inferences: ${results.hybridInferences}`);
console.log(` 📊 Avg Confidence: ${(results.avgConfidence * 100).toFixed(1)}%`);
console.log(` ⏱️ Duration: ${results.totalTime.toFixed(2)}ms`);
}
return results;
}
};
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