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209 строки
6.3 KiB
Markdown
209 строки
6.3 KiB
Markdown
---
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title: Active Inference Learning Path
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type: learning_path
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status: stable
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created: 2024-02-07
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tags:
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- active_inference
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- learning
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- progression
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semantic_relations:
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- type: implements
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links: [[learning_path_template]]
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- type: relates
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links:
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- [[knowledge_base/cognitive/active_inference]]
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- [[knowledge_base/mathematics/free_energy_theory]]
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---
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# Active Inference Learning Path
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## Overview
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This learning path guides you through understanding and implementing active inference, from foundational concepts to advanced applications. You'll learn the theoretical principles, mathematical foundations, and practical implementations.
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## Prerequisites
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### Required Knowledge
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- [[knowledge_base/mathematics/probability_theory|Probability Theory]]
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- [[knowledge_base/mathematics/information_theory|Information Theory]]
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- [[knowledge_base/mathematics/statistical_foundations|Statistical Foundations]]
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### Recommended Background
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- [[knowledge_base/cognitive/bayesian_brain|Bayesian Brain]]
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- [[knowledge_base/cognitive/predictive_processing|Predictive Processing]]
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- Python programming experience
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## Learning Progression
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### 1. Foundation (Week 1-2)
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#### Core Concepts
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- [[knowledge_base/cognitive/free_energy_principle|Free Energy Principle]]
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- [[knowledge_base/cognitive/predictive_processing|Predictive Processing]]
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- [[knowledge_base/cognitive/active_inference|Active Inference Basics]]
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#### Practical Exercises
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- [[examples/basic_belief_updating|Basic Belief Updating]]
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- [[examples/simple_prediction|Simple Prediction Exercise]]
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#### Learning Objectives
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- Understand the free energy principle
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- Grasp predictive processing fundamentals
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- Implement basic belief updating
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### 2. Mathematical Framework (Week 3-4)
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#### Advanced Concepts
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- [[knowledge_base/mathematics/variational_methods|Variational Methods]]
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- [[knowledge_base/mathematics/free_energy_theory|Free Energy Theory]]
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- [[knowledge_base/mathematics/expected_free_energy|Expected Free Energy]]
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#### Implementation Practice
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- [[examples/variational_inference|Variational Inference]]
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- [[examples/free_energy_computation|Free Energy Computation]]
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#### Learning Objectives
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- Master variational inference
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- Implement free energy computation
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- Understand expected free energy
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### 3. Implementation (Week 5-6)
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#### Core Components
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- [[knowledge_base/mathematics/belief_updating|Belief Updating]]
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- [[knowledge_base/mathematics/policy_selection|Policy Selection]]
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- [[knowledge_base/mathematics/action_distribution|Action Distribution]]
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#### Projects
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- [[examples/active_inference_basic|Basic Active Inference Agent]]
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- [[examples/pomdp_agent|POMDP Implementation]]
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#### Learning Objectives
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- Implement complete active inference agent
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- Master POMDP framework integration
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- Handle real-world applications
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### 4. Advanced Topics (Week 7-8)
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#### Specialized Areas
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- [[knowledge_base/mathematics/path_integral_theory|Path Integral Methods]]
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- [[knowledge_base/cognitive/hierarchical_processing|Hierarchical Models]]
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- [[knowledge_base/cognitive/social_cognition|Social Active Inference]]
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#### Advanced Projects
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- [[examples/hierarchical_agent|Hierarchical Agent]]
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- [[examples/multi_agent|Multi-Agent System]]
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#### Learning Objectives
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- Implement hierarchical models
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- Develop multi-agent systems
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- Apply to complex domains
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## Study Resources
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### Core Reading
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- [[knowledge_base/cognitive/free_energy_principle|Free Energy Principle]]
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- [[knowledge_base/mathematics/active_inference_theory|Active Inference Theory]]
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- [[knowledge_base/cognitive/active_inference|Active Inference Overview]]
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### Code Examples
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- [[examples/active_inference_basic|Basic Implementation]]
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- [[examples/pomdp_agent|POMDP Example]]
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- [[examples/hierarchical_agent|Hierarchical Example]]
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### Additional Resources
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- Research papers collection
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- Video tutorials
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- Community discussions
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## Assessment
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### Knowledge Checkpoints
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1. Foundation: Free energy and predictive processing
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2. Mathematics: Variational methods and inference
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3. Implementation: Agent architecture and POMDP
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4. Advanced: Hierarchical and multi-agent systems
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### Projects
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1. Mini-project: Basic belief updating system
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2. Implementation: Active inference agent
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3. Final project: Complex application domain
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### Success Criteria
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- Theoretical understanding demonstrated
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- Working implementations completed
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- Advanced concepts mastered
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- Real-world application developed
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## Next Steps
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### Advanced Paths
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- [[learning_paths/hierarchical_modeling|Hierarchical Modeling]]
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- [[learning_paths/multi_agent_systems|Multi-Agent Systems]]
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- [[learning_paths/robotics_control|Robotics Control]]
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### Specializations
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- [[specializations/neuroscience|Computational Neuroscience]]
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- [[specializations/robotics|Robotics and Control]]
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- [[specializations/ai|Artificial Intelligence]]
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## Related Paths
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### Prerequisites
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- [[learning_paths/probability_theory|Probability Theory]]
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- [[learning_paths/information_theory|Information Theory]]
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### Follow-up Paths
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- [[learning_paths/advanced_ai|Advanced AI]]
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- [[learning_paths/cognitive_architectures|Cognitive Architectures]]
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## Implementation Examples
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### Basic Examples
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```python
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# Basic active inference agent structure
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class ActiveInferenceAgent:
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def __init__(self, model_params):
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self.beliefs = initialize_beliefs()
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self.policies = generate_policies()
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def update_beliefs(self, observation):
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# Belief updating using variational inference
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pass
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def select_action(self):
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# Policy selection using expected free energy
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pass
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```
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### Advanced Implementation
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```python
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# Hierarchical active inference
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class HierarchicalAgent:
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def __init__(self, levels):
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self.levels = [
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ActiveInferenceAgent(level_params)
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for level_params in levels
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]
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def update(self, observation):
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# Hierarchical message passing
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for level in self.levels:
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level.update_beliefs(observation)
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prediction = level.generate_prediction()
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observation = prediction # For next level
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```
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## Common Challenges
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### Theoretical Challenges
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- Understanding variational inference
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- Grasping hierarchical processing
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- Interpreting free energy
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### Implementation Challenges
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- Numerical stability
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- Performance optimization
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- Model design
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### Solutions
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- Start with simple examples
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- Use provided templates
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- Follow progressive complexity |