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.

This repository implements a complete zone-guided reinforcement learning system, including self-play training, neural networks, and comparative tournaments against classic UCT.

Core Idea

The engine uses:

Displacement-based ABC Model with homogeneous coordinates
Dynamic Weighted Adjacency Matrices W = A ⊙ S ⊙ F
Hilbert curve ordering for efficient zone retrieval
A learned Zone Database that stores winning/losing position patterns
Zone Guidance (λ-PUCT) to bias search toward promising zones

For more information please refer to the paper at: https://doi.org/10.13140/RG.2.2.18795.09764

Files Overview

File	Purpose
`chess_mcvs.py`	Main implementation: game logic, ABC model, Zone Database, MCVS, neural networks, incremental training

Requirements

Install the minimal dependencies required to run chess_mcvs.py and the handler:

Notes

The repository contains the following important file:

chess_mcvs.py — main implementation (game logic, ABC model, zone DB, MCVS, networks)
For Hugging Face uploads, this README.md includes the model card front-matter (top YAML) and the requirements.txt lists the runtime dependencies.

chaim-duchovny/chess-model

Chess MCVS - Zone Guided AI

Core Idea

Files Overview

Requirements

Notes

chaim-duchovny/
chess-model