| ---
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| license: mit
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| pretty_name: Chess MCVS - Zone Guided AI
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| tags:
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| - chess
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| - game-ai
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| - monte-carlo-tree-search
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| - reinforcement-learning
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| - zone-guidance
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| - adjacency-matrix
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| - hilbert-curve
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| - abc-model
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| - pytorch
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| - numpy
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| task_categories:
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| - other
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| ---
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|
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| # Chess MCVS - Zone Guided AI
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|
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| **Advanced Monte-Carlo Value Search (MCVS)** engine for the game **Chess** (8x8), powered by a novel **Displacement-based ABC Model** and **Weighted Adjacency Matrices** with **Hilbert-ordered Zone Guidance**.
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| This repository implements a complete zone-guided reinforcement learning system, including self-play training, neural networks, and comparative tournaments against classic UCT.
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|
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| ## Core Idea
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| The engine uses:
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| - Displacement-based ABC Model with homogeneous coordinates
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| - Dynamic Weighted Adjacency Matrices `W = A ⊙ S ⊙ F`
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| - Hilbert curve ordering for efficient zone retrieval
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| - A learned **Zone Database** that stores winning/losing position patterns
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| - **Zone Guidance** (`λ-PUCT`) to bias search toward promising zones
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| For more information please refer to the paper at: https://doi.org/10.13140/RG.2.2.18795.09764
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|
|
| ## Files Overview
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|
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| | File | Purpose |
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| |----------------------------|--------|
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| | `chess_mcvs.py` | Main implementation: game logic, ABC model, Zone Database, MCVS, neural networks, incremental training |
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|
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| ## Requirements
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| Install the minimal dependencies required to run `chess_mcvs.py` and the handler:
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|
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| ## Notes
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|
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| The repository contains the following important file:
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| - `chess_mcvs.py` — main implementation (game logic, ABC model, zone DB, MCVS, networks)
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|
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| - For Hugging Face uploads, this `README.md` includes the model card front-matter (top YAML) and the `requirements.txt` lists the runtime dependencies.
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