Macro Learning Module Abstract

High-level Purpose and Responsibility

The macro learning module implements automated discovery and learning of higher-order patterns and chunks in sequential learning tasks. It identifies recurring subsequences, builds hierarchical representations of learned material, and enables efficient learning and recall through pattern recognition and chunking strategies.

Key Data Structures and Relationships

  • MacroDiscovery: Pattern detection system for identifying recurring subsequences in learning tasks
  • ChunkHierarchy: Multi-level representation of learned patterns from individual items to complex macros
  • PatternLibrary: Repository of discovered patterns with frequency and utility statistics
  • MacroApplication: System for applying learned patterns to accelerate new learning
  • SequenceDecomposition: Breaking down complex sequences into learned chunks and novel elements
  • PatternGeneralization: Extension of specific patterns to broader contexts and variations

Main Data Flows and Transformations

  1. Pattern Detection: Learning sequences → Statistical analysis → Discovery of recurring subsequences
  2. Chunk Formation: Detected patterns → Hierarchical organization → Multi-level pattern library
  3. Pattern Application: New sequences → Pattern matching → Acceleration of learning through chunking
  4. Hierarchical Compression: Low-level patterns → Composition → Higher-order macro structures
  5. Transfer Learning: Learned patterns → Application to novel domains → Cross-context skill transfer

External Dependencies and Interfaces

  • Learning Module: Integration with core learner state for pattern-based skill development
  • Tasks Module: Sequence analysis and generation leveraging discovered patterns
  • Statistics Module: Statistical significance testing for pattern discovery and frequency analysis
  • Protocol Module: Versioning and reproducibility of discovered pattern libraries

State Management Patterns

  • Incremental Pattern Discovery: Continuous updating of pattern library with new learning experiences
  • Pattern Strength Tracking: Maintains usage frequency and success rates for different patterns
  • Hierarchical Pattern Organization: Multi-level storage and retrieval of patterns at different abstraction levels
  • Context-Dependent Activation: Dynamic pattern selection based on current learning context

Core Algorithms or Business Logic Abstractions

  • Suffix Tree Construction: Efficient data structures for pattern discovery in sequential data
  • Statistical Pattern Mining: Frequency-based and significance-based pattern identification algorithms
  • Hierarchical Clustering: Organization of patterns into meaningful hierarchical structures
  • Pattern Compression: Identification of optimal chunking strategies for memory efficiency
  • Context-Sensitive Pattern Application: Dynamic selection of patterns based on current learning context
  • Meta-Pattern Discovery: Learning about patterns of patterns and higher-order regularities