Episodic memory
Last updated
Last updated
The Episodic Memory System captures, stores, and reviews successful task execution patterns. It combines traditional memory storage with advanced learning capabilities to create a self-improving system that adapts to changes in tools and discovers better solution paths over time.
Success Paths stores in episodic memory:
Success Paths Success paths represent dynamic connections formed when new neural nodes interact with the network. These paths are:
Self-organizing sequences that emerge from node interactions
Probability-weighted based on successful outcomes
Capable of combining short and long paths
Continuously updated through node activities
Success paths can be:
Short success paths (immediate node-to-node connections)
Long success paths (multi-node interaction chains)
Combined success paths (merged short and long pathways = Long success paths)
Memory Structure The neural system organizes connections in three layers:
Node Properties
Connection strength
Interaction probability
Activation threshold
Response patterns
Path Formation
Short path generation
Long path development
Path combination rules
Success validation
Learning Dynamics
Path strength reinforcement
Interaction frequency
Success probability
Adaptation patterns
The system uses reinforcement learning to:
Evaluate path effectiveness
Identify improvement opportunities
Optimize tool selection
Adapt to changing conditions
The learning process considers:
Success rates of different approaches
Tool reliability and performance
Resource efficiency
Execution time and costs
Paths evolve through:
Performance tracking
Success pattern identification
Alternative path discovery
Automated optimization
The system actively manages tool dependencies:
Monitors tool health and reliability
Identifies alternative tools
Adapts paths when tools become unavailable
Discovers new tool combinations
When tools fail or become unreliable:
Find alternative tools with similar capabilities
Modify paths to use available tools
Generate new approaches using different tools
Learn from successful adaptations
Paths evolve through several stages:
Initial Creation
Basic success pattern documentation
Tool dependency mapping
Performance baseline establishment
Optimization
Performance improvement
Tool optimization
Resource efficiency enhancement
Adaptation
Tool replacement handling
Alternative path discovery
New approach generation
Maturation
Reliable performance patterns
Stable tool relationships
Proven success rates
The system learns through:
Direct Experience
Execution outcomes
Performance metrics
Failure patterns
Success indicators
Pattern Recognition
Common success elements
Reliable tool combinations
Efficient resource usage
Optimal step sequences
Adaptive Improvement
Tool reliability patterns
Alternative approach discovery
Performance optimization
Resource efficiency
Continuous Improvement
Paths become more efficient over time
System learns from each execution
Performance continually optimizes
Resource usage improves
Reliability
Handles tool failures gracefully
Maintains service continuity
Provides consistent results
Adapts to changes
Innovation
Discovers new approaches
Combines successful patterns
Generates alternative solutions
Evolves with experience
Advanced Learning
Cross-path learning
Meta-pattern recognition
Predictive optimization
Collaborative learning
Tool Evolution
Automated tool discovery
Capability prediction
Tool combination optimization
Performance forecasting
Path Enhancement
Dynamic path generation
Real-time optimization
Context-aware adaptation
Hybrid path creation
Vector Embedding Layer
Input Processing
Node attributes converted to numerical vectors
Contextual information embedded using transformer model
Dynamic dimension reduction to maintain efficiency
Real-time vector generation for new nodes
Embedding Model Components
Primary transformer for node attribute encoding
Context encoder for environmental conditions
Relationship encoder for node connections
Temporal encoder for sequence patterns
Vector Database Architecture
Storage Structure
Partitioned indexes for short and long paths
Hierarchical clustering for similar path patterns
Metadata store for node properties and relationships
Cache layer for frequently accessed patterns
Retrieval System
Approximate Nearest Neighbor (ANN) search for path matching
Multi-vector lookup for combined paths
Similarity threshold filtering
Priority queuing for high-probability paths
Memory Management System
Vector Operations
Path merging through vector concatenation
Distance calculation for node relationships
Weighted averaging for path combinations
Dynamic vector updates for learning
Optimization Layer
Automatic index rebalancing
Vector compression for storage efficiency
Cached results for common patterns
Background cleanup for unused paths
Integration Components
Node Processing Pipeline
Vector generation for new nodes
Real-time similarity matching
Path probability calculation
Dynamic path updates
System Interfaces
API for node creation and updates
Query interface for path retrieval
Monitoring endpoints for system health
Batch processing for offline learning
The Episodic Memory System represents a living, learning system that not only stores successful task patterns but actively evolves them through experience. By combining memory storage with reinforcement learning and tool adaptation, it creates a robust system that improves over time and handles changes in its environment effectively.
The system's ability to learn from experience, adapt to tool changes, and discover new approaches makes it particularly valuable for long-term task optimization and reliability. As it continues to evolve, the system becomes increasingly efficient and capable of handling complex task sequences with greater success rates.