/** * Feedback Loop System for Behavior Modification * Enables the system to learn from outcomes and modify behavior dynamically */ export interface FeedbackSignal { id: string; source: string; type: 'success' | 'failure' | 'partial' | 'unexpected' | 'novel'; action: string; outcome: any; expected: any; surprise: number; utility: number; timestamp: number; context: any; } export interface BehaviorModification { component: string; parameter: string; oldValue: any; newValue: any; reason: string; confidence: number; timestamp: number; expectedImprovement: number; } export interface AdaptationRule { trigger: (feedback: FeedbackSignal) => boolean; modification: (feedback: FeedbackSignal, currentState: any) => BehaviorModification[]; priority: number; learningRate: number; category: string; } export declare class FeedbackLoopSystem { private feedbackHistory; private behaviorModifications; private adaptationRules; private behaviorParameters; private performanceMetrics; private learningCurves; constructor(); /** * Process feedback and trigger behavior modifications */ processFeedback(feedback: FeedbackSignal): Promise; /** * Register new adaptation rule */ registerAdaptationRule(rule: AdaptationRule): void; /** * Create feedback loop for continuous improvement */ createContinuousImprovementLoop(component: string, metric: string): void; /** * Implement reinforcement learning feedback loop */ createReinforcementLoop(actionSpace: string[], rewardFunction: (outcome: any) => number): void; /** * Create exploration-exploitation feedback loop */ createExplorationExploitationLoop(explorationRate?: number): void; /** * Implement meta-learning feedback loop */ createMetaLearningLoop(): void; /** * Create adaptive complexity feedback loop */ createComplexityAdaptationLoop(): void; /** * Apply behavior modification to system parameters */ private applyBehaviorModification; /** * Learn from feedback patterns to create new adaptation rules */ private learnFromFeedbackPattern; /** * Initialize default adaptation rules */ private initializeDefaultRules; /** * Initialize default behavior parameters */ private initializeDefaultParameters; /** * Update performance metrics based on feedback */ private updatePerformanceMetrics; /** * Calculate performance score from feedback */ private calculatePerformanceScore; /** * Get current behavior state */ private getCurrentBehaviorState; /** * Get metric trend for analysis */ private getMetricTrend; /** * Check if metric is improving */ private isMetricImproving; /** * Generate improvement modifications */ private generateImprovementModifications; /** * Update action probabilities based on reinforcement learning */ private updateActionProbabilities; /** * Analyze learning effectiveness */ private analyzeLearningEffectiveness; /** * Adjust learning parameters based on effectiveness */ private adjustLearningParameters; /** * Get recent performance trend */ private getRecentPerformanceTrend; /** * Adapt complexity based on performance */ private adaptComplexity; /** * Update learning curve for component */ private updateLearningCurve; /** * Detect failure patterns in recent feedback */ private detectFailurePattern; /** * Detect success patterns in recent feedback */ private detectSuccessPattern; /** * Create adaptation rule from detected pattern */ private createRuleFromPattern; /** * Create reinforcement rule from success pattern */ private createReinforcementRule; /** * Find common elements across contexts */ private findCommonElements; /** * Get feedback loop statistics */ getStats(): any; private getMostActiveComponents; private getAdaptationCategories; }