Development of A Machine Learning Based Chess Game in Python

Dwi Hastuti; WILDAN surya Wijaya

doi:10.36456/best.vol7.no1.10343

Authors

Dwi Hastuti University of PGRI Adi Buana Surabaya
Wildan Surya Wijaya University of PGRI Adibuana Surabaya https://orcid.org/0009-0001-6502-0286

DOI:

https://doi.org/10.36456/best.vol7.no1.10343

Keywords:

Machine Learning, Chess Game, Neural Networks, Supervised Learning, Phyton

Abstract

This paper presents the design and implementation of a Machine Learning (ML)-based chess game developed in Python. Unlike traditional chess game that rely primarily on alpha-beta pruning and handcrafted evaluation functions, this approach employs supervised learning techniques to create a neural network model capable of evaluating chess positions and making move decisions. The system leverages the python-chess library for game representation and the scikit-learn framework for implementing the machine learning components. We demonstrate that even with relatively simple feature engineering and a modest neural network architecture, the system can learn effective chess strategies. The implementation is designed to run in a Jupyter Notebook environment, providing an interactive interface for human players to compete against the ML agent while facilitating educational insights into both chess strategy and machine learning principles.

Author Biographies

Dwi Hastuti, University of PGRI Adi Buana Surabaya

Electrical Engineering
Wildan Surya Wijaya, University of PGRI Adibuana Surabaya

Electrical Engineering

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