New Hybrid Graph Convolution Neural Network with Applications in Game Strategy

Hanyue Xu; Kah Phooi Seng; Li-Minn Ang

doi:10.3390/electronics12194020

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New Hybrid Graph Convolution Neural Network with Applications in Game Strategy

Journal article

Open access

Peer reviewed

New Hybrid Graph Convolution Neural Network with Applications in Game Strategy

Hanyue Xu, Kah Phooi Seng and Li-Minn Ang

Electronics, Vol.12(19), pp.1-17

2023

DOI: https://doi.org/10.3390/electronics12194020

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Abstract

graph convolutional networks

deep convolutional neural networks

game strategies

Deep convolutional neural networks (DCNNs) have enjoyed much success in many applications, such as computer vision, automated medical diagnosis, autonomous systems, etc. Another application of DCNNs is for game strategies, where the deep neural network architecture can be used to directly represent and learn strategies from expert players on different sides. Many game states can be expressed not only as a matrix data structure suitable for DCNN training but also as a graph data structure. Most of the available DCNN methods ignore the territory characteristics of both sides’ positions based on the game rules. Therefore, in this paper, we propose a hybrid approach to the graph neural network to extract the features of the model of game-playing strategies and fuse it into a DCNN. As a graph learning model, graph convolutional networks (GCNs) provide a scheme by which to extract the features in a graph structure, which can better extract the features in the relationship between the game-playing strategies. We validate the work and design a hybrid network to integrate GCNs and DCNNs in the game of Go and show that on the KGS Go dataset, the performance of the hybrid model outperforms the traditional DCNN model. The hybrid model demonstrates a good performance in extracting the game strategy of Go.

Details

Title: New Hybrid Graph Convolution Neural Network with Applications in Game Strategy
Authors: Hanyue Xu (Author) - Xi’an Jiaotong-Liverpool University
Kah Phooi Seng (Corresponding Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Li-Minn Ang (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Publication details: Electronics, Vol.12(19), pp.1-17
Publisher: MDPI AG
Date published: 2023
DOI: 10.3390/electronics12194020
ISSN: 2079-9292
Copyright note: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Organisation Unit: School of Health - Biomedicine; School of Health - Nursing; School of Science, Technology and Engineering; Engage Research Lab; External; School of Health - Paramedicine
Language: English
Record Identifier: 99969890302621
Output Type: Journal article

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