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131 строка
2.9 KiB
Markdown
131 строка
2.9 KiB
Markdown
# InferAnt Stream 10: Active Inference - Modeling, Learning, and Exploration
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## Stream Information
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- **Platform**: GitHub Live
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- **Repository**: <https://github.com/docxology/cognitive>
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- **Tools**:
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- Obsidian: <https://obsidian.md/>
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- CodeViz: <https://codeviz.ai/>
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## Agenda
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### 2. Theoretical Foundations
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- Active Inference Framework
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- Free Energy Principle review
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- Generative models
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- Belief updating
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- Policy selection
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- Learning and Exploration
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- Epistemic value
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- Expected free energy
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- Exploration-exploitation balance
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- Information gain
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### 3. Implementation Architecture
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- POMDP (simple and generic), Ants, Biofirms
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- CodeViz and More.
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- Core Components
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- GenerativeModel class
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- BeliefUpdater class
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- PolicySelector class
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- FreeEnergyCalculator class
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- Matrix Requirements
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- A matrix (observation mapping)
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- B matrix (transition dynamics)
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- C matrix (preference encoding)
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- D matrix (prior beliefs)
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- E matrix (policy specification)
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### 4. Code Development
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- Base Implementation
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- Matrix initialization and validation
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- Belief updating mechanisms
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- Policy evaluation functions
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- Action selection methods
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- Testing Framework
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- Unit tests setup
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- Integration tests
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- Visualization tests
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- Property-based tests
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### 5. Practical Applications
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- Example Scenarios
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- Simple navigation task
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- Multi-agent coordination
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- Resource foraging
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- Pattern learning
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- Visualization Methods
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- State space plots
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- Belief evolution
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- Free energy landscapes
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- Policy evaluation
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### 6. Future Directions
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- Next Steps
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- Extended functionality
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- Performance optimization
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- Additional test cases
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- Documentation improvements
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- Community Engagement
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- Contribution guidelines
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- Issue tracking
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- Feature requests
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- Collaboration opportunities
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## Repository Organization
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```
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cognitive/
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├── src/
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│ ├── active_inference/
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│ │ ├── __init__.py
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│ │ ├── generative_model.py
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│ │ ├── belief_updater.py
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│ │ └── policy_selector.py
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│ └── utils/
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│ ├── visualization.py
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│ └── validation.py
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├── tests/
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│ ├── unit/
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│ ├── integration/
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│ └── visualization/
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├── docs/
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│ ├── theory/
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│ ├── implementation/
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│ └── examples/
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└── examples/
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├── navigation/
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├── foraging/
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└── pattern_learning/
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```
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## Next Steps
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1. Implement core classes and functions
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2. Develop comprehensive test suite
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3. Create visualization utilities
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4. Document API and examples
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5. Integrate with existing codebase
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6. Establish contribution workflow
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## References
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- Free Energy Principle foundations
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- Active Inference implementations
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- Related cognitive architectures
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- Relevant research papers
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## Notes
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- Focus on modular, reusable components
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- Maintain clear documentation
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- Ensure test coverage
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- Consider performance optimization
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- Enable easy extension
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