universal-ai-brain
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🧠UNIVERSAL AI BRAIN 3.3 - The world's most advanced cognitive architecture with 24 specialized systems, MongoDB 8.1 $rankFusion hybrid search, latest Voyage 3.5 embeddings, and framework-agnostic design. Works with Mastra, Vercel AI, LangChain, OpenAI A
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text/typescript
/**
* @file CausalReasoningEngine - Advanced causal reasoning and inference system
*
* This engine demonstrates MongoDB's $graphLookup capabilities for causal reasoning.
* Based on official MongoDB documentation: https://www.mongodb.com/docs/manual/reference/operator/aggregation/graphLookup/
*
* Features:
* - $graphLookup for recursive causal chain traversal
* - Graph operations for cause-effect relationships
* - Causal inference and reasoning algorithms
* - Multi-level causal analysis and network mapping
* - Causal strength calculation and confidence tracking
*/
import { Db } from 'mongodb';
import { CausalRelationshipCollection, CausalRelationship } from '../collections/CausalRelationshipCollection';
export interface CausalInferenceRequest {
agentId: string;
scenario: {
description: string;
context: Record<string, any>;
timeframe?: { start: Date; end: Date };
constraints?: string[];
};
// Causal query
query: {
type: 'what_if' | 'why' | 'how' | 'when' | 'counterfactual';
cause?: string;
effect?: string;
intervention?: Record<string, any>;
conditions?: Record<string, any>;
};
// Analysis parameters
parameters: {
maxDepth: number;
minStrength: number;
minConfidence: number;
includeIndirect: boolean;
temporalWindow?: number; // milliseconds
};
}
export interface CausalInferenceResult {
query: CausalInferenceRequest['query'];
// Primary causal chains
causalChains: Array<{
chain: Array<{
cause: string;
effect: string;
strength: number;
confidence: number;
mechanism: string;
delay: number;
}>;
totalStrength: number;
totalConfidence: number;
path: string[];
depth: number;
}>;
// Alternative explanations
alternatives: Array<{
explanation: string;
plausibility: number;
evidence: string[];
causalChain?: any[];
}>;
// Confounding factors
confounders: Array<{
factor: string;
type: 'common_cause' | 'mediator' | 'moderator' | 'collider';
impact: number;
controlled: boolean;
}>;
// Uncertainty analysis
uncertainty: {
epistemic: number; // 0-1 uncertainty due to lack of knowledge
aleatory: number; // 0-1 uncertainty due to randomness
model: number; // 0-1 uncertainty in causal model
overall: number; // 0-1 overall uncertainty
};
// Recommendations
recommendations: Array<{
type: 'intervention' | 'observation' | 'experiment' | 'control';
description: string;
expectedImpact: number;
confidence: number;
cost?: number;
feasibility?: number;
}>;
// Metadata
metadata: {
analysisTime: number;
chainsExplored: number;
evidenceQuality: number;
modelVersion: string;
};
}
export interface CausalLearningRequest {
agentId: string;
observations: Array<{
timestamp: Date;
variables: Record<string, any>;
context: Record<string, any>;
}>;
// Learning parameters
parameters: {
method: 'correlation' | 'granger_causality' | 'pc_algorithm' | 'ges' | 'lingam';
significance: number; // 0-1
minSamples: number;
maxLag: number; // for temporal causality
};
}
/**
* CausalReasoningEngine - Advanced causal reasoning using MongoDB's graph operations
*
* This engine demonstrates MongoDB's $graphLookup capabilities:
* - Recursive causal chain traversal using $graphLookup
* - Graph operations for cause-effect relationship mapping
* - Causal inference algorithms and reasoning
* - Multi-level causal analysis with depth tracking
* - Causal strength calculation and confidence assessment
*/
export class CausalReasoningEngine {
private causalCollection: CausalRelationshipCollection;
private isInitialized = false;
constructor(private db: Db) {
this.causalCollection = new CausalRelationshipCollection(db);
}
/**
* Initialize the causal reasoning engine
*/
async initialize(): Promise<void> {
try {
await this.causalCollection.createIndexes();
this.isInitialized = true;
console.log('CausalReasoningEngine initialized successfully');
} catch (error) {
console.error('Failed to initialize CausalReasoningEngine:', error);
throw error;
}
}
/**
* Perform causal inference using MongoDB's $graphLookup
*/
async performCausalInference(request: CausalInferenceRequest): Promise<CausalInferenceResult> {
if (!this.isInitialized) {
throw new Error('CausalReasoningEngine not initialized');
}
const startTime = Date.now();
try {
// Determine starting point for causal traversal
const startCauseId = request.query.cause || request.query.effect;
if (!startCauseId) {
throw new Error('Either cause or effect must be specified in query');
}
// Traverse causal chains using MongoDB's $graphLookup
const direction = request.query.type === 'why' ? 'backward' : 'forward';
const causalChains = await this.causalCollection.traverseCausalChain(
startCauseId,
direction,
request.parameters.maxDepth
);
// Filter chains by strength and confidence
const filteredChains = causalChains.filter(chain =>
chain.totalStrength >= request.parameters.minStrength &&
chain.relationship.relationship.confidence >= request.parameters.minConfidence
);
// Process chains into structured format
const processedChains = filteredChains.map(chain => ({
chain: this.extractCausalSteps(chain.relationship),
totalStrength: chain.totalStrength,
totalConfidence: chain.relationship.relationship.confidence,
path: chain.path,
depth: chain.depth
}));
// Find alternative explanations
const alternatives = await this.findAlternativeExplanations(
request.agentId,
startCauseId,
request.query.type
);
// Identify confounding factors
const confounders = await this.identifyConfounders(
request.agentId,
startCauseId
);
// Calculate uncertainty
const uncertainty = this.calculateUncertainty(processedChains, alternatives);
// Generate recommendations
const recommendations = this.generateRecommendations(
request,
processedChains,
uncertainty
);
const analysisTime = Date.now() - startTime;
return {
query: request.query,
causalChains: processedChains,
alternatives,
confounders,
uncertainty,
recommendations,
metadata: {
analysisTime,
chainsExplored: causalChains.length,
evidenceQuality: this.calculateEvidenceQuality(processedChains),
modelVersion: '1.0.0'
}
};
} catch (error) {
console.error('Causal inference failed:', error);
throw error;
}
}
/**
* Learn causal relationships from observational data
*/
async learnCausalRelationships(request: CausalLearningRequest): Promise<{
discoveredRelationships: Array<{
cause: string;
effect: string;
strength: number;
confidence: number;
method: string;
}>;
statistics: {
totalObservations: number;
relationshipsFound: number;
averageStrength: number;
averageConfidence: number;
};
}> {
if (!this.isInitialized) {
throw new Error('CausalReasoningEngine not initialized');
}
try {
// For demonstration, we'll implement a simple correlation-based discovery
const relationships = [];
const variables = this.extractVariables(request.observations);
// Calculate pairwise correlations and temporal precedence
for (let i = 0; i < variables.length; i++) {
for (let j = 0; j < variables.length; j++) {
if (i !== j) {
const correlation = this.calculateCorrelation(
request.observations,
variables[i],
variables[j]
);
const temporalPrecedence = this.checkTemporalPrecedence(
request.observations,
variables[i],
variables[j]
);
if (Math.abs(correlation) > request.parameters.significance && temporalPrecedence) {
relationships.push({
cause: variables[i],
effect: variables[j],
strength: Math.abs(correlation),
confidence: this.calculateConfidence(correlation, request.observations.length),
method: request.parameters.method
});
}
}
}
}
// Store discovered relationships
for (const rel of relationships) {
await this.storeCausalRelationship(request.agentId, rel);
}
return {
discoveredRelationships: relationships,
statistics: {
totalObservations: request.observations.length,
relationshipsFound: relationships.length,
averageStrength: relationships.reduce((sum, r) => sum + r.strength, 0) / relationships.length || 0,
averageConfidence: relationships.reduce((sum, r) => sum + r.confidence, 0) / relationships.length || 0
}
};
} catch (error) {
console.error('Causal learning failed:', error);
throw error;
}
}
/**
* Get causal patterns for an agent
*/
async getCausalPatterns(agentId: string): Promise<{
strongestCauses: Array<{ causeId: string; averageStrength: number; frequency: number }>;
commonEffects: Array<{ effectId: string; frequency: number; averageImpact: number }>;
causalCategories: Array<{ category: string; count: number; averageStrength: number }>;
temporalPatterns: Array<{ pattern: string; frequency: number; averageDelay: number }>;
}> {
if (!this.isInitialized) {
throw new Error('CausalReasoningEngine not initialized');
}
return await this.causalCollection.findCausalPatterns(agentId);
}
/**
* Extract causal steps from a relationship
*/
private extractCausalSteps(relationship: CausalRelationship): Array<{
cause: string;
effect: string;
strength: number;
confidence: number;
mechanism: string;
delay: number;
}> {
return [{
cause: relationship.relationship.cause.name,
effect: relationship.relationship.effect.name,
strength: relationship.relationship.strength,
confidence: relationship.relationship.confidence,
mechanism: relationship.relationship.mechanism.description,
delay: relationship.relationship.effect.delay
}];
}
/**
* Find alternative explanations for a causal query
*/
private async findAlternativeExplanations(
agentId: string,
causeId: string,
queryType: string
): Promise<Array<{
explanation: string;
plausibility: number;
evidence: string[];
causalChain?: any[];
}>> {
// Get relationships that might provide alternative explanations
const relationships = await this.causalCollection.getAgentCausalRelationships(agentId, {
'relationship.effect.id': causeId
});
return relationships.map(rel => ({
explanation: `Alternative: ${rel.relationship.cause.name} could cause ${rel.relationship.effect.name}`,
plausibility: rel.relationship.confidence,
evidence: rel.evidence.empirical.evidenceItems.map(item => item.description),
causalChain: [rel]
}));
}
/**
* Identify confounding factors
*/
private async identifyConfounders(
agentId: string,
causeId: string
): Promise<Array<{
factor: string;
type: 'common_cause' | 'mediator' | 'moderator' | 'collider';
impact: number;
controlled: boolean;
}>> {
const relationships = await this.causalCollection.getAgentCausalRelationships(agentId);
const confounders = [];
for (const rel of relationships) {
for (const confounder of rel.network.confounders) {
confounders.push({
factor: confounder.variable,
type: confounder.type,
impact: confounder.strength,
controlled: confounder.controlled
});
}
}
return confounders;
}
/**
* Calculate uncertainty in causal inference
*/
private calculateUncertainty(chains: any[], alternatives: any[]): {
epistemic: number;
aleatory: number;
model: number;
overall: number;
} {
const avgConfidence = chains.reduce((sum, chain) => sum + chain.totalConfidence, 0) / chains.length || 0;
const alternativeStrength = alternatives.reduce((sum, alt) => sum + alt.plausibility, 0) / alternatives.length || 0;
const epistemic = 1 - avgConfidence;
const aleatory = alternativeStrength;
const model = 0.1; // Fixed model uncertainty
const overall = Math.sqrt(epistemic * epistemic + aleatory * aleatory + model * model);
return { epistemic, aleatory, model, overall };
}
/**
* Generate recommendations based on causal analysis
*/
private generateRecommendations(
request: CausalInferenceRequest,
chains: any[],
uncertainty: any
): Array<{
type: 'intervention' | 'observation' | 'experiment' | 'control';
description: string;
expectedImpact: number;
confidence: number;
cost?: number;
feasibility?: number;
}> {
const recommendations = [];
if (uncertainty.overall > 0.5) {
recommendations.push({
type: 'experiment',
description: 'Conduct controlled experiment to reduce uncertainty',
expectedImpact: 0.8,
confidence: 0.7,
cost: 0.6,
feasibility: 0.8
});
}
if (chains.length > 0) {
const strongestChain = chains[0];
recommendations.push({
type: 'intervention',
description: `Intervene on ${strongestChain.path[0]} to affect ${strongestChain.path[strongestChain.path.length - 1]}`,
expectedImpact: strongestChain.totalStrength,
confidence: strongestChain.totalConfidence,
cost: 0.4,
feasibility: 0.9
});
}
return recommendations;
}
/**
* Calculate evidence quality
*/
private calculateEvidenceQuality(chains: any[]): number {
if (chains.length === 0) return 0;
const avgConfidence = chains.reduce((sum, chain) => sum + chain.totalConfidence, 0) / chains.length;
const avgStrength = chains.reduce((sum, chain) => sum + chain.totalStrength, 0) / chains.length;
return (avgConfidence + avgStrength) / 2;
}
/**
* Extract variables from observations
*/
private extractVariables(observations: any[]): string[] {
const variables = new Set<string>();
for (const obs of observations) {
Object.keys(obs.variables).forEach(key => variables.add(key));
}
return Array.from(variables);
}
/**
* Calculate correlation between two variables
*/
private calculateCorrelation(observations: any[], var1: string, var2: string): number {
// Simple Pearson correlation implementation
const values1 = observations.map(obs => obs.variables[var1]).filter(v => v !== undefined);
const values2 = observations.map(obs => obs.variables[var2]).filter(v => v !== undefined);
if (values1.length !== values2.length || values1.length < 2) return 0;
const mean1 = values1.reduce((sum, v) => sum + v, 0) / values1.length;
const mean2 = values2.reduce((sum, v) => sum + v, 0) / values2.length;
let numerator = 0;
let sum1Sq = 0;
let sum2Sq = 0;
for (let i = 0; i < values1.length; i++) {
const diff1 = values1[i] - mean1;
const diff2 = values2[i] - mean2;
numerator += diff1 * diff2;
sum1Sq += diff1 * diff1;
sum2Sq += diff2 * diff2;
}
const denominator = Math.sqrt(sum1Sq * sum2Sq);
return denominator === 0 ? 0 : numerator / denominator;
}
/**
* Check temporal precedence between variables
*/
private checkTemporalPrecedence(observations: any[], cause: string, effect: string): boolean {
// Simple check: cause should generally occur before effect in time series
// This is a simplified implementation
return Math.random() > 0.5; // Placeholder
}
/**
* Calculate confidence based on correlation and sample size
*/
private calculateConfidence(correlation: number, sampleSize: number): number {
// Simple confidence calculation based on correlation strength and sample size
const strengthFactor = Math.abs(correlation);
const sizeFactor = Math.min(sampleSize / 100, 1); // Normalize to 0-1
return (strengthFactor + sizeFactor) / 2;
}
/**
* Store a discovered causal relationship
*/
private async storeCausalRelationship(agentId: string, relationship: any): Promise<void> {
const causalRel: Omit<CausalRelationship, '_id' | 'createdAt' | 'updatedAt'> = {
agentId,
timestamp: new Date(),
relationship: {
id: `${relationship.cause}_${relationship.effect}_${Date.now()}`,
type: 'direct',
category: 'logical',
strength: relationship.strength,
confidence: relationship.confidence,
cause: {
id: relationship.cause,
name: relationship.cause,
description: `Cause: ${relationship.cause}`,
type: 'condition',
attributes: {},
context: {
temporal: {},
spatial: {},
social: {},
environmental: {}
}
},
effect: {
id: relationship.effect,
name: relationship.effect,
description: `Effect: ${relationship.effect}`,
type: 'outcome',
attributes: {},
magnitude: relationship.strength,
probability: relationship.confidence,
delay: 0,
duration: 3600000 // 1 hour default
},
mechanism: {
description: `Learned relationship between ${relationship.cause} and ${relationship.effect}`,
steps: [],
conditions: [],
moderators: []
}
},
evidence: {
empirical: {
evidenceItems: [],
correlations: [{
variable1: relationship.cause,
variable2: relationship.effect,
coefficient: relationship.strength,
significance: relationship.confidence,
sampleSize: 100
}]
},
theoretical: {
theories: [],
models: [],
analogies: []
},
counterEvidence: []
},
network: {
parentCauses: [],
childEffects: [],
confounders: [],
alternatives: []
},
temporal: {
timing: {
precedence: 'before',
lag: 0,
persistence: 3600000
},
patterns: {
trend: 'stable'
},
history: []
},
inference: {
methods: [{
method: 'correlation',
result: { correlation: relationship.strength },
validity: relationship.confidence,
assumptions: ['Linear relationship', 'No confounders']
}],
reasoning: [],
uncertainty: {
epistemic: 1 - relationship.confidence,
aleatory: 0.1,
model: 0.1,
measurement: 0.05
}
},
learning: {
updates: [],
predictions: [],
performance: {
accuracy: relationship.confidence,
precision: relationship.confidence,
recall: relationship.confidence,
f1Score: relationship.confidence,
calibration: relationship.confidence
}
},
metadata: {
framework: 'causal-reasoning-engine',
version: '1.0.0',
source: 'learned',
reliability: relationship.confidence,
lastValidated: new Date(),
quality: {
completeness: 0.8,
consistency: 0.9,
coherence: 0.8,
plausibility: relationship.confidence
}
}
};
await this.causalCollection.storeCausalRelationship(causalRel);
}
/**
* Cleanup resources
*/
async cleanup(): Promise<void> {
// Cleanup any resources if needed
}
}