Online Match Prediction in Shogi Using Deep Convolutional Neural Networks

Jim O’Connor, Melanie Fernández

2024

Abstract

This paper presents a novel approach to online evaluation of shogi games using Deep Convolutional Neural Networks (DCNNs). Shogi, a complex deterministic abstract strategy game, poses unique challenges due to its extensive game tree and the dynamic nature of piece movement, including the ability to play captured pieces. Traditional methods of game evaluation for shogi rely on either expert knowledge and handcrafted heuristics, or prohibitively high computational costs and limited scalability. Our method promotes a unique dataset of shogi game records and SFEN (Forsyth Edward Notation) strings to convert board positions into binary representations, which are then fed into a DCNN. The DCNN architecture, tailored for shogi board analysis, consists of convolutional and fully connected layers culminating in a binary classification output indicating a winning or losing position. Training the DCNN on approximately one million board states resulted in an 82.7% classification accuracy on a validation set. Our approach allows for online single board evaluation, while offering a computationally efficient alternative to traditional methods, paving the way for the development of additional shogi evaluation methods without the need for extensive expert knowledge or computational resources.

Paper Citation

in Harvard Style

O’Connor J. and Fernández M. (2024). Online Match Prediction in Shogi Using Deep Convolutional Neural Networks. In Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA; ISBN 978-989-758-721-4, SciTePress, pages 600-605. DOI: 10.5220/0013018100003837

in Bibtex Style

@conference{ncta24,
author={Jim O’Connor and Melanie Fernández},
title={Online Match Prediction in Shogi Using Deep Convolutional Neural Networks},
booktitle={Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA},
year={2024},
pages={600-605},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013018100003837},
isbn={978-989-758-721-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA
TI - Online Match Prediction in Shogi Using Deep Convolutional Neural Networks
SN - 978-989-758-721-4
AU - O’Connor J.
AU - Fernández M.
PY - 2024
SP - 600
EP - 605
DO - 10.5220/0013018100003837
PB - SciTePress