cognitive/docs/concepts/key_concepts.md

# Key Concepts

## Overview
This guide explains the core concepts of our cognitive modeling framework, integrating Active Inference with knowledge management.

## Knowledge Structure

### Nodes
Fundamental units of knowledge representation:

#### Agent Nodes
- [[agent_template|Agent Template]]
- Represent cognitive agents
- Contain beliefs, goals, and actions
- See [[agent_concepts]]

#### Belief Nodes
- [[belief_template|Belief Template]]
- Represent probabilistic beliefs
- Update through observations
- See [[belief_concepts]]

#### Goal Nodes
- [[goal_template|Goal Template]]
- Define agent objectives
- Guide policy selection
- See [[goal_concepts]]

### Relationships
- Bidirectional links between nodes
- Represent dependencies and influences
- Follow [[linking_patterns]]

## Active Inference Framework

### Core Principles
1. Free Energy Minimization
   - Drives agent behavior
   - Balances exploration/exploitation
   - See [[free_energy_principle]]

2. Belief Updating
   - Bayesian inference
   - Precision-weighted updates
   - See [[belief_updating]]

3. Policy Selection
   - Expected free energy
   - Action-perception cycles
   - See [[policy_selection]]

### Implementation
- [[active_inference_intro]]
- [[model_architecture]]
- [[implementation_guide]]

## Tool Integration

### Development Environment
- [[cursor_integration]]
  - AI-augmented development
  - Code generation
  - Documentation assistance

### Knowledge Management
- [[obsidian_usage]]
  - Network visualization
  - Bidirectional linking
  - Template system

### Version Control
- [[git_workflow]]
  - Code management
  - Knowledge base versioning
  - Collaboration

## Modeling Paradigms

### Knowledge Representation
1. Template-Based
   - Structured templates
   - Consistent metadata
   - See [[template_guide]]

2. Network-Centric
   - Graph structure
   - Relationship mapping
   - See [[network_analysis]]

3. Version-Controlled
   - Change tracking
   - Collaboration
   - See [[version_control]]

### Cognitive Architecture

#### Belief Systems
- Probabilistic representations
- Prior knowledge
- Learning mechanisms
- See [[belief_systems]]

#### Goal Hierarchies
- Objective structures
- Priority management
- Temporal horizons
- See [[goal_hierarchies]]

#### Action Selection
- Policy inference
- Expected free energy
- Decision making
- See [[action_selection]]

## Development Workflow

### Code Development
1. Use [[cursor_integration]]
2. Follow [[code_style]]
3. Update [[documentation_guide]]

### Knowledge Management
1. Use [[obsidian_usage]]
2. Maintain [[linking_patterns]]
3. Follow [[maintenance_guide]]

### Version Control
1. Follow [[git_workflow]]
2. Use [[branching_strategy]]
3. Review [[deployment_guide]]

## Best Practices

### Code Organization
- Modular structure
- Clear dependencies
- See [[code_organization]]

### Documentation
- Comprehensive guides
- Clear examples
- See [[documentation_guide]]

### Testing
- Unit tests
- Integration tests
- See [[testing_guide]]

## Advanced Topics

### Network Analysis
- Graph metrics
- Relationship patterns
- See [[network_analysis]]

### Model Optimization
- Parameter tuning
- Performance analysis
- See [[optimization_guide]]

### Scaling
- Distributed systems
- Performance optimization
- See [[scaling_guide]]

## Related Concepts
- [[active_inference_theory]]
- [[cognitive_architectures]]
- [[knowledge_graphs]]
- [[probabilistic_modeling]]

## References
- [[research_papers]]
- [[implementation_examples]]
- [[further_reading]]