cognitive/InferAnt_Stream_10.md

InferAnt Stream 10: Active Inference - Modeling, Learning, and Exploration

Stream Information

• Platform: GitHub Live
• Repository: https://github.com/docxology/cognitive
• Tools:
  • Obsidian: https://obsidian.md/
  • CodeViz: https://codeviz.ai/

Agenda

2. Theoretical Foundations

• Active Inference Framework
  • Free Energy Principle review
  • Generative models
  • Belief updating
  • Policy selection
• Learning and Exploration
  • Epistemic value
  • Expected free energy
  • Exploration-exploitation balance
  • Information gain

3. Implementation Architecture

• POMDP (simple and generic), Ants, Biofirms
• CodeViz and More.
• Core Components
  • GenerativeModel class
  • BeliefUpdater class
  • PolicySelector class
  • FreeEnergyCalculator class
• Matrix Requirements
  • A matrix (observation mapping)
  • B matrix (transition dynamics)
  • C matrix (preference encoding)
  • D matrix (prior beliefs)
  • E matrix (policy specification)

4. Code Development

• Base Implementation
  • Matrix initialization and validation
  • Belief updating mechanisms
  • Policy evaluation functions
  • Action selection methods
• Testing Framework
  • Unit tests setup
  • Integration tests
  • Visualization tests
  • Property-based tests

5. Practical Applications

• Example Scenarios
  • Simple navigation task
  • Multi-agent coordination
  • Resource foraging
  • Pattern learning
• Visualization Methods
  • State space plots
  • Belief evolution
  • Free energy landscapes
  • Policy evaluation

6. Future Directions

• Next Steps
  • Extended functionality
  • Performance optimization
  • Additional test cases
  • Documentation improvements
• Community Engagement
  • Contribution guidelines
  • Issue tracking
  • Feature requests
  • Collaboration opportunities

Repository Organization

cognitive/
├── src/
│   ├── active_inference/
│   │   ├── __init__.py
│   │   ├── generative_model.py
│   │   ├── belief_updater.py
│   │   └── policy_selector.py
│   └── utils/
│       ├── visualization.py
│       └── validation.py
├── tests/
│   ├── unit/
│   ├── integration/
│   └── visualization/
├── docs/
│   ├── theory/
│   ├── implementation/
│   └── examples/
└── examples/
    ├── navigation/
    ├── foraging/
    └── pattern_learning/

Next Steps

1. Implement core classes and functions
2. Develop comprehensive test suite
3. Create visualization utilities
4. Document API and examples
5. Integrate with existing codebase
6. Establish contribution workflow

References

• Free Energy Principle foundations
• Active Inference implementations
• Related cognitive architectures
• Relevant research papers

Notes

• Focus on modular, reusable components
• Maintain clear documentation
• Ensure test coverage
• Consider performance optimization
• Enable easy extension