From 4e0653b73bdecc17241f015aee38297d61c82590 Mon Sep 17 00:00:00 2001 From: Daniel Ari Friedman Date: Fri, 7 Feb 2025 13:55:09 -0800 Subject: [PATCH] Ending of the stream 010.1 --- .obsidian/graph.json | 2 +- .obsidian/workspace.json | 4 +- Things/Baseball_Game/Baseball_Game.md | 201 ++++++++++++++++++++++++++ 3 files changed, 204 insertions(+), 3 deletions(-) create mode 100644 Things/Baseball_Game/Baseball_Game.md diff --git a/.obsidian/graph.json b/.obsidian/graph.json index 0012d93..f3f853d 100644 --- a/.obsidian/graph.json +++ b/.obsidian/graph.json @@ -17,6 +17,6 @@ "repelStrength": 10, "linkStrength": 1, "linkDistance": 250, - "scale": 0.10124266675309304, + "scale": 0.07829474440680095, "close": false } \ No newline at end of file diff --git a/.obsidian/workspace.json b/.obsidian/workspace.json index 2da99f9..8bcdaa2 100644 --- a/.obsidian/workspace.json +++ b/.obsidian/workspace.json @@ -185,6 +185,8 @@ }, "active": "283b4112120e60a0", "lastOpenFiles": [ + "Things/Baseball_Game/Baseball_Game.md", + "Things/Baseball_Game", "Things/Path_Network/venv/lib/python3.10/site-packages/pip-25.0.dist-info/top_level.txt", "Things/Path_Network/venv/lib/python3.10/site-packages/pip-25.0.dist-info/entry_points.txt", "Things/Path_Network/venv/lib/python3.10/site-packages/pip-25.0.dist-info/WHEEL", @@ -194,8 +196,6 @@ "Things/Path_Network/venv/lib/python3.10/site-packages/pip-25.0.dist-info/LICENSE.txt", "Things/Path_Network/venv/lib/python3.10/site-packages/pip-25.0.dist-info/AUTHORS.txt", "Things/Path_Network/venv/lib/python3.10/site-packages/pip-25.0.dist-info/INSTALLER", - "Things/Path_Network/venv/lib/python3.10/site-packages/pip-25.0.dist-info", - "Things/Path_Network/venv/bin/Activate.ps1", "Things/Path_Network/output/20250207_134114/agent_height_variations.png", "Things/Path_Network/output/20250207_134114/water_level_distribution.png", "Things/Path_Network/output/20250207_134114/final_state.png", diff --git a/Things/Baseball_Game/Baseball_Game.md b/Things/Baseball_Game/Baseball_Game.md new file mode 100644 index 0000000..56b44ef --- /dev/null +++ b/Things/Baseball_Game/Baseball_Game.md @@ -0,0 +1,201 @@ +# Baseball Game Active Inference Simulation Manifesto + +## Overview +This document outlines the theoretical framework and implementation strategy for modeling a baseball game using active inference principles. The simulation aims to capture the complex dynamics of baseball through the lens of free energy minimization and belief updating. + +## Core Principles + +### 1. Active Inference Framework +- Players and teams modeled as active inference agents +- Free energy minimization drives decision-making +- Hierarchical generative models represent game states +- Precision-weighted belief updating based on sensory evidence +- Action selection through expected free energy minimization + +### 2. Multi-Agent System Architecture + +#### Agents +1. **Players** + - Individual active inference models + - Position-specific priors and policies + - Continuous state-space representation + - Proprioceptive and exteroceptive modalities + +2. **Teams** + - Collective active inference at team level + - Shared generative models + - Strategic coordination through belief alignment + - Hierarchical policy selection + +3. **Umpires** + - Objective state observers + - Rule enforcement agents + - Precision modulators for game flow + +### 3. State Space Representation + +#### Physical States +- Ball position, velocity, and spin (6D state space) +- Player positions and orientations +- Field geometry and boundaries +- Weather conditions and environmental factors + +#### Game States +- Inning structure +- Score tracking +- Count (balls/strikes) +- Base occupancy +- Game phase indicators + +#### Mental States +- Player confidence levels +- Team momentum +- Strategic intentions +- Risk assessment metrics + +### 4. Action Space + +#### Player Actions +- Batting mechanics +- Pitching variations +- Fielding movements +- Base running decisions + +#### Team Actions +- Defensive positioning +- Batting order optimization +- Pitching changes +- Strategic timeouts + +### 5. Generative Models + +#### Hierarchical Structure +1. **Low-level physics** + - Ball trajectories + - Collision dynamics + - Player movement physics + +2. **Mid-level gameplay** + - Play outcomes + - Situation-specific strategies + - Performance statistics + +3. **High-level strategy** + - Game flow + - Win probability + - Long-term planning + +### 6. Learning and Adaptation + +#### Model Parameters +- Prior beliefs updated through experience +- Precision parameters tuned dynamically +- Policy preferences refined by outcomes +- Skill development through practice + +#### Team Dynamics +- Emergent strategies +- Role specialization +- Coordination patterns +- Adaptive responses + +### 7. Implementation Strategy + +#### Technical Architecture +1. **Simulation Engine** + - Physics-based core + - Event-driven architecture + - Real-time processing capability + - Modular component design + +2. **Data Collection** + - State tracking + - Action logging + - Performance metrics + - Belief evolution + +3. **Visualization** + - 3D rendering + - Statistical displays + - Belief visualization + - Decision trees + +#### Development Phases +1. **Phase 1: Core Mechanics** + - Basic physics implementation + - Simple agent models + - Fundamental game rules + +2. **Phase 2: Active Inference Integration** + - Generative model implementation + - Free energy computation + - Policy selection mechanisms + +3. **Phase 3: Learning and Adaptation** + - Parameter updating + - Strategy evolution + - Performance optimization + +4. **Phase 4: Advanced Features** + - Complex strategies + - Team dynamics + - Environmental factors + +### 8. Research Objectives + +#### Primary Goals +1. Demonstrate active inference in complex sports dynamics +2. Model emergent team strategies +3. Study skill acquisition and adaptation +4. Analyze decision-making under uncertainty + +#### Applications +- Training and strategy development +- Player development systems +- Game outcome prediction +- Performance analysis + +### 9. Evaluation Metrics + +#### Performance Metrics +- Win-loss records +- Statistical accuracy +- Strategy effectiveness +- Learning efficiency + +#### Model Metrics +- Free energy minimization +- Belief convergence +- Policy optimization +- Prediction accuracy + +## Future Directions + +### Extensions +1. Multi-game seasons +2. Player development trajectories +3. Team chemistry modeling +4. Injury and fatigue effects + +### Integration Opportunities +1. Real game data validation +2. Machine learning hybridization +3. Virtual reality training +4. Strategic analysis tools + +## Technical Requirements + +### Software Stack +- Python-based simulation core +- Physics engine integration +- Neural network components +- Visualization toolkit + +### Computing Resources +- GPU acceleration for physics +- Parallel processing for agents +- Real-time visualization +- Data storage and analysis + +## Conclusion +This baseball simulation framework provides a comprehensive platform for studying active inference in complex sports environments, offering insights into both individual and team dynamics while maintaining computational tractability and practical applicability. \ No newline at end of file