universal-ai-brain/packages/core/src/intelligence/ConfidenceTrackingEngine.ts

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/**
 * @file ConfidenceTrackingEngine - Advanced uncertainty quantification for AI agents
 * 
 * This engine provides comprehensive confidence tracking and calibration using MongoDB's
 * statistical aggregation capabilities. Demonstrates MongoDB's advanced analytics for
 * multi-dimensional confidence data and uncertainty quantification.
 * 
 * Features:
 * - Multi-dimensional confidence tracking with statistical aggregations
 * - Uncertainty quantification (epistemic vs aleatoric)
 * - Confidence calibration and prediction accuracy analysis
 * - Real-time confidence monitoring and alerting
 * - Adaptive confidence adjustment based on historical performance
 * - Temporal confidence modeling with decay functions
 */

import { Db, ObjectId } from 'mongodb';
import { ConfidenceTrackingCollection, ConfidenceRecord } from '../collections/ConfidenceTrackingCollection';

export interface ConfidenceAssessmentRequest {
  agentId: string;
  sessionId?: string;
  task: string;
  taskType: 'prediction' | 'classification' | 'generation' | 'reasoning' | 'decision';
  domain: string;
  complexity: number; // 0-1
  novelty: number; // 0-1
  stakes: 'low' | 'medium' | 'high' | 'critical';
  prediction: {
    type: 'binary' | 'multiclass' | 'regression' | 'ranking' | 'generation';
    value: any;
    alternatives?: Array<{ value: any; confidence: number; reasoning: string }>;
    probability?: number;
  };
  features: string[];
  computationTime: number;
  memoryUsage?: number;
}

export interface ConfidenceAssessment {
  confidenceId: ObjectId;
  overall: number;
  breakdown: {
    epistemic: number; // Knowledge uncertainty
    aleatoric: number; // Data uncertainty
    calibrated: number; // Historically adjusted
  };
  aspects: {
    factualAccuracy: number;
    completeness: number;
    relevance: number;
    clarity: number;
    appropriateness: number;
  };
  sources: {
    modelIntrinsic: number;
    retrievalQuality: number;
    contextRelevance: number;
    historicalPerformance: number;
    domainExpertise: number;
  };
  recommendations: string[];
  riskLevel: 'low' | 'medium' | 'high' | 'critical';
  shouldProceed: boolean;
}

export interface ConfidenceCalibration {
  isWellCalibrated: boolean;
  calibrationError: number;
  overconfidenceRate: number;
  underconfidenceRate: number;
  brierScore: number;
  logLoss: number;
  reliability: number;
  resolution: number;
  sharpness: number;
  recommendations: string[];
}

export interface ConfidenceTrends {
  timeline: Array<{
    timestamp: Date;
    avgConfidence: number;
    accuracy: number;
    calibrationError: number;
    predictionCount: number;
  }>;
  trends: {
    confidenceTrend: 'improving' | 'stable' | 'declining';
    accuracyTrend: 'improving' | 'stable' | 'declining';
    calibrationTrend: 'improving' | 'stable' | 'declining';
  };
  insights: string[];
}

/**
 * ConfidenceTrackingEngine - Advanced uncertainty quantification for AI agents
 * 
 * This engine showcases MongoDB's statistical aggregation capabilities:
 * - Complex aggregation pipelines for confidence analytics
 * - Statistical functions for calibration analysis
 * - Time-series optimization for confidence tracking
 * - Multi-dimensional confidence modeling
 * - Real-time confidence monitoring and alerting
 */
export class ConfidenceTrackingEngine {
  private db: Db;
  private confidenceCollection: ConfidenceTrackingCollection;
  private isInitialized: boolean = false;

  // Confidence tracking configuration
  private config = {
    calibration: {
      minSamplesForCalibration: 10,
      calibrationWindow: 30, // days
      targetCalibrationError: 0.1,
      overconfidenceThreshold: 0.8,
      underconfidenceThreshold: 0.5
    },
    confidence: {
      defaultDecayRate: 0.05, // per hour
      defaultHalfLife: 24, // hours
      minConfidenceThreshold: 0.3,
      maxConfidenceThreshold: 0.95
    },
    monitoring: {
      alertThresholds: {
        calibrationError: 0.2,
        accuracyDrop: 0.1,
        overconfidenceRate: 0.3
      }
    }
  };

  constructor(db: Db) {
    this.db = db;
    this.confidenceCollection = new ConfidenceTrackingCollection(db);
  }

  /**
   * Initialize the confidence tracking engine
   */
  async initialize(): Promise<void> {
    if (this.isInitialized) {
      return;
    }

    try {
      // Create collection indexes
      await this.confidenceCollection.createIndexes();
      
      this.isInitialized = true;
      console.log('🤔 ConfidenceTrackingEngine initialized successfully');
    } catch (error) {
      console.error('❌ Failed to initialize ConfidenceTrackingEngine:', error);
      throw error;
    }
  }

  /**
   * Assess confidence for a prediction or decision
   */
  async assessConfidence(request: ConfidenceAssessmentRequest): Promise<ConfidenceAssessment> {
    if (!this.isInitialized) {
      throw new Error('ConfidenceTrackingEngine must be initialized first');
    }

    // Get historical performance for calibration
    const historicalPerformance = await this.getHistoricalPerformance(
      request.agentId,
      request.domain,
      request.taskType
    );

    // Calculate multi-dimensional confidence
    const confidence = this.calculateMultiDimensionalConfidence(request, historicalPerformance);
    
    // Create confidence record
    const confidenceRecord: Omit<ConfidenceRecord, '_id' | 'createdAt' | 'updatedAt'> = {
      agentId: request.agentId,
      sessionId: request.sessionId,
      timestamp: new Date(),
      context: {
        task: request.task,
        taskType: request.taskType,
        domain: request.domain,
        complexity: request.complexity,
        novelty: request.novelty,
        stakes: request.stakes
      },
      confidence: {
        overall: confidence.overall,
        epistemic: confidence.breakdown.epistemic,
        aleatoric: confidence.breakdown.aleatoric,
        calibrated: confidence.breakdown.calibrated,
        aspects: confidence.aspects,
        sources: confidence.sources
      },
      prediction: request.prediction,
      temporal: {
        decayRate: this.config.confidence.defaultDecayRate,
        halfLife: this.config.confidence.defaultHalfLife,
        expiresAt: new Date(Date.now() + (this.config.confidence.defaultHalfLife * 60 * 60 * 1000))
      },
      learning: {
        surprisal: 0, // Will be calculated when actual outcome is known
        informationGain: 0,
        modelUpdate: false,
        confidenceAdjustment: 0
      },
      metadata: {
        framework: 'universal-ai-brain',
        model: 'confidence-tracking-v1',
        version: '1.0.0',
        features: request.features,
        computationTime: request.computationTime,
        memoryUsage: request.memoryUsage
      }
    };

    // Store confidence record
    const confidenceId = await this.confidenceCollection.recordConfidence(confidenceRecord);

    // Generate recommendations and risk assessment
    const recommendations = this.generateConfidenceRecommendations(confidence);
    const riskLevel = this.assessRiskLevel(confidence, request.stakes);
    const shouldProceed = this.shouldProceedWithPrediction(confidence, riskLevel);

    return {
      confidenceId,
      overall: confidence.overall,
      breakdown: confidence.breakdown,
      aspects: confidence.aspects,
      sources: confidence.sources,
      recommendations,
      riskLevel,
      shouldProceed
    };
  }

  /**
   * Update confidence record with actual outcome for calibration
   */
  async updateWithActualOutcome(
    confidenceId: ObjectId,
    actualValue: any,
    correct: boolean,
    accuracy?: number,
    feedback?: string,
    verificationSource: 'automatic' | 'human' | 'external_system' = 'automatic'
  ): Promise<void> {
    const actual = {
      value: actualValue,
      correct,
      accuracy,
      feedback,
      verificationTime: new Date(),
      verificationSource
    };

    await this.confidenceCollection.updateWithActual(confidenceId, actual);
  }

  /**
   * Analyze confidence calibration for an agent
   */
  async analyzeCalibration(agentId: string, days: number = 30): Promise<ConfidenceCalibration> {
    const calibrationAnalysis = await this.confidenceCollection.analyzeCalibration(agentId, {
      timeRange: {
        start: new Date(Date.now() - (days * 24 * 60 * 60 * 1000)),
        end: new Date()
      }
    });

    const stats = await this.confidenceCollection.getConfidenceStats(agentId, days);

    const isWellCalibrated = calibrationAnalysis.ece < this.config.calibration.targetCalibrationError;
    const recommendations = this.generateCalibrationRecommendations(calibrationAnalysis, stats);

    return {
      isWellCalibrated,
      calibrationError: calibrationAnalysis.ece,
      overconfidenceRate: stats.overconfidenceRate,
      underconfidenceRate: stats.underconfidenceRate,
      brierScore: calibrationAnalysis.brierScore,
      logLoss: calibrationAnalysis.logLoss,
      reliability: calibrationAnalysis.calibrationCurve.length > 0 ? 
        calibrationAnalysis.calibrationCurve.reduce((sum, point) => sum + point.accuracy, 0) / calibrationAnalysis.calibrationCurve.length : 0,
      resolution: calibrationAnalysis.mce,
      sharpness: stats.avgConfidence,
      recommendations
    };
  }

  /**
   * Get confidence trends over time
   */
  async getConfidenceTrends(agentId: string, days: number = 30): Promise<ConfidenceTrends> {
    const timeline = await this.confidenceCollection.getConfidenceTrends(agentId, days, 'day');
    
    // Calculate trends
    const trends = this.calculateTrends(timeline);
    const insights = this.generateTrendInsights(timeline, trends);

    return {
      timeline,
      trends,
      insights
    };
  }

  /**
   * Get confidence statistics for an agent
   */
  async getConfidenceStats(agentId: string, days: number = 7): Promise<{
    totalPredictions: number;
    verifiedPredictions: number;
    avgConfidence: number;
    accuracy: number;
    calibrationError: number;
    overconfidenceRate: number;
    underconfidenceRate: number;
    confidenceByDomain: Array<{ domain: string; avgConfidence: number; accuracy: number }>;
    performanceMetrics: {
      avgComputationTime: number;
      avgMemoryUsage: number;
      efficiency: number;
    };
  }> {
    const stats = await this.confidenceCollection.getConfidenceStats(agentId, days);
    
    // Calculate performance metrics
    const performanceMetrics = await this.calculatePerformanceMetrics(agentId, days);

    return {
      ...stats,
      performanceMetrics
    };
  }

  /**
   * Monitor confidence in real-time and generate alerts
   */
  async monitorConfidence(agentId: string): Promise<{
    alerts: Array<{
      type: 'calibration_error' | 'accuracy_drop' | 'overconfidence' | 'underconfidence';
      severity: 'low' | 'medium' | 'high' | 'critical';
      message: string;
      recommendations: string[];
    }>;
    status: 'healthy' | 'warning' | 'critical';
  }> {
    const stats = await this.getConfidenceStats(agentId, 7);
    const calibration = await this.analyzeCalibration(agentId, 7);
    
    const alerts = [];

    // Check calibration error
    if (calibration.calibrationError > this.config.monitoring.alertThresholds.calibrationError) {
      alerts.push({
        type: 'calibration_error' as const,
        severity: calibration.calibrationError > 0.3 ? 'critical' : 'high',
        message: `High calibration error: ${calibration.calibrationError.toFixed(3)}`,
        recommendations: ['Review confidence assessment methods', 'Increase training data', 'Adjust confidence thresholds']
      });
    }

    // Check accuracy drop
    if (stats.accuracy < 0.7) {
      alerts.push({
        type: 'accuracy_drop' as const,
        severity: stats.accuracy < 0.5 ? 'critical' : 'medium',
        message: `Low accuracy: ${stats.accuracy.toFixed(3)}`,
        recommendations: ['Review model performance', 'Update training data', 'Check for domain shift']
      });
    }

    // Check overconfidence
    if (stats.overconfidenceRate > this.config.monitoring.alertThresholds.overconfidenceRate) {
      alerts.push({
        type: 'overconfidence' as const,
        severity: stats.overconfidenceRate > 0.5 ? 'high' : 'medium',
        message: `High overconfidence rate: ${stats.overconfidenceRate.toFixed(3)}`,
        recommendations: ['Lower confidence thresholds', 'Increase uncertainty estimates', 'Add confidence penalties']
      });
    }

    // Determine overall status
    const status = alerts.some(a => a.severity === 'critical') ? 'critical' :
                   alerts.some(a => a.severity === 'high') ? 'warning' : 'healthy';

    return { alerts, status };
  }

  /**
   * Calculate multi-dimensional confidence
   */
  private calculateMultiDimensionalConfidence(
    request: ConfidenceAssessmentRequest,
    historicalPerformance: any
  ): ConfidenceAssessment {
    // Base confidence from prediction probability or heuristics
    const baseConfidence = request.prediction.probability || this.estimateBaseConfidence(request);

    // Epistemic uncertainty (knowledge-based)
    const epistemic = this.calculateEpistemicUncertainty(request, historicalPerformance);

    // Aleatoric uncertainty (data-based)
    const aleatoric = this.calculateAleatoricUncertainty(request);

    // Calibrated confidence based on historical performance
    const calibrated = this.calibrateConfidence(baseConfidence, historicalPerformance);

    // Aspect-based confidence breakdown
    const aspects = {
      factualAccuracy: Math.max(0.1, baseConfidence - (request.novelty * 0.2)),
      completeness: Math.max(0.1, baseConfidence - (request.complexity * 0.15)),
      relevance: Math.max(0.1, baseConfidence - (epistemic * 0.3)),
      clarity: Math.max(0.1, baseConfidence - (aleatoric * 0.2)),
      appropriateness: Math.max(0.1, baseConfidence - (request.novelty * 0.1))
    };

    // Confidence sources
    const sources = {
      modelIntrinsic: baseConfidence,
      retrievalQuality: Math.max(0.1, 0.8 - (request.novelty * 0.3)),
      contextRelevance: Math.max(0.1, 0.9 - (request.complexity * 0.2)),
      historicalPerformance: historicalPerformance.avgAccuracy || 0.5,
      domainExpertise: Math.max(0.1, 0.8 - (request.novelty * 0.4))
    };

    // Overall confidence (weighted combination)
    const overall = Math.min(0.95, Math.max(0.05, 
      (baseConfidence * 0.4) + 
      (calibrated * 0.3) + 
      ((1 - epistemic) * 0.2) + 
      ((1 - aleatoric) * 0.1)
    ));

    return {
      confidenceId: new ObjectId(), // Temporary, will be replaced
      overall,
      breakdown: {
        epistemic,
        aleatoric,
        calibrated
      },
      aspects,
      sources,
      recommendations: [],
      riskLevel: 'medium',
      shouldProceed: true
    };
  }

  /**
   * Get historical performance for calibration
   */
  private async getHistoricalPerformance(
    agentId: string,
    domain: string,
    taskType: string
  ): Promise<{
    avgAccuracy: number;
    avgConfidence: number;
    calibrationError: number;
    sampleCount: number;
  }> {
    const stats = await this.confidenceCollection.getConfidenceStats(agentId, 30);
    const domainStats = stats.confidenceByDomain.find(d => d.domain === domain);

    return {
      avgAccuracy: domainStats?.accuracy || stats.accuracy || 0.5,
      avgConfidence: domainStats?.avgConfidence || stats.avgConfidence || 0.5,
      calibrationError: stats.calibrationError || 0.2,
      sampleCount: stats.verifiedPredictions || 0
    };
  }

  /**
   * Estimate base confidence from request characteristics
   */
  private estimateBaseConfidence(request: ConfidenceAssessmentRequest): number {
    // Heuristic-based confidence estimation
    let confidence = 0.7; // Base confidence

    // Adjust for complexity
    confidence -= request.complexity * 0.2;

    // Adjust for novelty
    confidence -= request.novelty * 0.3;

    // Adjust for stakes (higher stakes = more conservative)
    const stakesAdjustment = {
      low: 0.1,
      medium: 0.05,
      high: -0.05,
      critical: -0.1
    };
    confidence += stakesAdjustment[request.stakes];

    // Adjust for task type
    const taskTypeConfidence = {
      classification: 0.8,
      prediction: 0.7,
      generation: 0.6,
      reasoning: 0.65,
      decision: 0.75
    };
    confidence = (confidence + taskTypeConfidence[request.taskType]) / 2;

    return Math.min(0.95, Math.max(0.05, confidence));
  }

  /**
   * Calculate epistemic uncertainty (knowledge-based)
   */
  private calculateEpistemicUncertainty(
    request: ConfidenceAssessmentRequest,
    historicalPerformance: any
  ): number {
    let uncertainty = 0.2; // Base epistemic uncertainty

    // Higher uncertainty for novel tasks
    uncertainty += request.novelty * 0.3;

    // Higher uncertainty for complex tasks
    uncertainty += request.complexity * 0.2;

    // Lower uncertainty with more historical data
    if (historicalPerformance.sampleCount > 10) {
      uncertainty -= Math.min(0.2, historicalPerformance.sampleCount / 100);
    }

    return Math.min(0.9, Math.max(0.05, uncertainty));
  }

  /**
   * Calculate aleatoric uncertainty (data-based)
   */
  private calculateAleatoricUncertainty(request: ConfidenceAssessmentRequest): number {
    let uncertainty = 0.15; // Base aleatoric uncertainty

    // Task type affects inherent uncertainty
    const taskTypeUncertainty = {
      classification: 0.1,
      prediction: 0.2,
      generation: 0.25,
      reasoning: 0.15,
      decision: 0.18
    };
    uncertainty = taskTypeUncertainty[request.taskType];

    // Domain complexity affects uncertainty
    uncertainty += request.complexity * 0.1;

    return Math.min(0.8, Math.max(0.05, uncertainty));
  }

  /**
   * Calibrate confidence based on historical performance
   */
  private calibrateConfidence(baseConfidence: number, historicalPerformance: any): number {
    if (historicalPerformance.sampleCount < this.config.calibration.minSamplesForCalibration) {
      return baseConfidence;
    }

    // Adjust based on historical calibration error
    const adjustment = historicalPerformance.calibrationError * 
      (historicalPerformance.avgConfidence > historicalPerformance.avgAccuracy ? -1 : 1);

    return Math.min(0.95, Math.max(0.05, baseConfidence + adjustment));
  }

  /**
   * Generate confidence recommendations
   */
  private generateConfidenceRecommendations(confidence: ConfidenceAssessment): string[] {
    const recommendations = [];

    if (confidence.overall < 0.5) {
      recommendations.push('Consider gathering more information before proceeding');
      recommendations.push('Review input data quality and completeness');
    }

    if (confidence.breakdown.epistemic > 0.6) {
      recommendations.push('High knowledge uncertainty - consider domain expert consultation');
      recommendations.push('Increase training data for this domain');
    }

    if (confidence.breakdown.aleatoric > 0.5) {
      recommendations.push('High data uncertainty - verify input data quality');
      recommendations.push('Consider ensemble methods to reduce uncertainty');
    }

    if (confidence.aspects.factualAccuracy < 0.6) {
      recommendations.push('Low factual accuracy confidence - verify facts before proceeding');
    }

    return recommendations;
  }

  /**
   * Assess risk level based on confidence and stakes
   */
  private assessRiskLevel(
    confidence: ConfidenceAssessment,
    stakes: string
  ): 'low' | 'medium' | 'high' | 'critical' {
    const stakesWeight = { low: 0.25, medium: 0.5, high: 0.75, critical: 1.0 };
    const riskScore = (1 - confidence.overall) * stakesWeight[stakes as keyof typeof stakesWeight];

    if (riskScore > 0.7) return 'critical';
    if (riskScore > 0.5) return 'high';
    if (riskScore > 0.3) return 'medium';
    return 'low';
  }

  /**
   * Determine if should proceed with prediction
   */
  private shouldProceedWithPrediction(
    confidence: ConfidenceAssessment,
    riskLevel: string
  ): boolean {
    const thresholds = {
      low: 0.3,
      medium: 0.5,
      high: 0.7,
      critical: 0.9
    };

    return confidence.overall >= thresholds[riskLevel as keyof typeof thresholds];
  }

  /**
   * Generate calibration recommendations
   */
  private generateCalibrationRecommendations(
    calibrationAnalysis: any,
    stats: any
  ): string[] {
    const recommendations = [];

    if (calibrationAnalysis.ece > 0.15) {
      recommendations.push('Improve confidence calibration through temperature scaling');
      recommendations.push('Collect more diverse training data');
    }

    if (stats.overconfidenceRate > 0.3) {
      recommendations.push('Reduce overconfidence by lowering confidence thresholds');
      recommendations.push('Implement confidence penalties in training');
    }

    if (stats.underconfidenceRate > 0.3) {
      recommendations.push('Address underconfidence by improving model training');
      recommendations.push('Review uncertainty estimation methods');
    }

    return recommendations;
  }

  /**
   * Calculate trends from timeline data
   */
  private calculateTrends(timeline: any[]): {
    confidenceTrend: 'improving' | 'stable' | 'declining';
    accuracyTrend: 'improving' | 'stable' | 'declining';
    calibrationTrend: 'improving' | 'stable' | 'declining';
  } {
    if (timeline.length < 3) {
      return {
        confidenceTrend: 'stable',
        accuracyTrend: 'stable',
        calibrationTrend: 'stable'
      };
    }

    const recent = timeline.slice(-7);
    const older = timeline.slice(0, Math.max(1, timeline.length - 7));

    const recentAvgConfidence = recent.reduce((sum, t) => sum + t.avgConfidence, 0) / recent.length;
    const olderAvgConfidence = older.reduce((sum, t) => sum + t.avgConfidence, 0) / older.length;

    const recentAvgAccuracy = recent.reduce((sum, t) => sum + (t.accuracy || 0), 0) / recent.length;
    const olderAvgAccuracy = older.reduce((sum, t) => sum + (t.accuracy || 0), 0) / older.length;

    const recentAvgCalibration = recent.reduce((sum, t) => sum + (t.calibrationError || 0), 0) / recent.length;
    const olderAvgCalibration = older.reduce((sum, t) => sum + (t.calibrationError || 0), 0) / older.length;

    return {
      confidenceTrend: this.determineTrend(recentAvgConfidence, olderAvgConfidence),
      accuracyTrend: this.determineTrend(recentAvgAccuracy, olderAvgAccuracy),
      calibrationTrend: this.determineTrend(olderAvgCalibration, recentAvgCalibration) // Lower is better for calibration error
    };
  }

  /**
   * Determine trend direction
   */
  private determineTrend(recent: number, older: number): 'improving' | 'stable' | 'declining' {
    const change = (recent - older) / older;
    if (change > 0.05) return 'improving';
    if (change < -0.05) return 'declining';
    return 'stable';
  }

  /**
   * Generate trend insights
   */
  private generateTrendInsights(timeline: any[], trends: any): string[] {
    const insights = [];

    if (trends.confidenceTrend === 'declining') {
      insights.push('Confidence levels are declining - review model performance');
    }

    if (trends.accuracyTrend === 'improving') {
      insights.push('Accuracy is improving - current approach is working well');
    }

    if (trends.calibrationTrend === 'declining') {
      insights.push('Calibration is getting worse - review confidence assessment methods');
    }

    if (timeline.length > 0) {
      const avgPredictions = timeline.reduce((sum, t) => sum + t.predictionCount, 0) / timeline.length;
      if (avgPredictions > 100) {
        insights.push('High prediction volume - ensure quality is maintained');
      }
    }

    return insights;
  }

  /**
   * Calculate performance metrics
   */
  private async calculatePerformanceMetrics(agentId: string, days: number): Promise<{
    avgComputationTime: number;
    avgMemoryUsage: number;
    efficiency: number;
  }> {
    // This would typically use aggregation to calculate performance metrics
    // For now, return simulated metrics
    return {
      avgComputationTime: 150, // ms
      avgMemoryUsage: 25, // MB
      efficiency: 0.85 // Efficiency score
    };
  }

  /**
   * Cleanup expired confidence records
   */
  async cleanup(): Promise<number> {
    return await this.confidenceCollection.cleanupExpiredRecords();
  }
}