A synthetic player for Ayὸ board game using alpha-beta search and learning vector quantization

Ayilara, O.A; Ajayi, Anuoluwapo O.; Jimoh, KA

Abstract

Game playing especially, Ayὸ game has been an important topic of research in artificial intelligence and several machine learning approaches have been used, but the need to optimize computing resources is important to encourage the significant interest of users. This study presents a synthetic player (Ayὸ) implemented using Alpha-beta search and Learning Vector Quantization network. The program for the board game was written in Java and MATLAB. Evaluation of the synthetic player was carried out in terms of the win percentage and game length. The synthetic player had a better efficiency compared to the traditional Alpha-beta search algorithm.

Citation

Ayilara, O., Ajayi, A. O., & Jimoh, K. (2016). A synthetic player for Ayὸ board game using alpha-beta search and learning vector quantization. Computer and Information Science, 9(3), 1-6. https://doi.org/10.5539/cis.v9n3p1

• Journal Article Type: Article
• Acceptance Date: May 6, 2016
• Publication Date: Jun 19, 2016
• Deposit Date: Apr 23, 2018
• Publicly Available Date: Apr 23, 2018
• Journal: Computer and Information Science
• Print ISSN: 1913-8989
• Publisher: Canadian Center of Science and Education
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Issue: 3
• Pages: 1-6
• DOI: https://doi.org/10.5539/cis.v9n3p1
• Keywords: intelligence, board game, win ratio, computing resources
• Public URL: https://uwe-repository.worktribe.com/output/910883
• Publisher URL: http://dx.doi.org/10.5539/cis.v9n3p1