A Synthetic Player for Ayὸ Board Game Using Alpha-Beta Search and Learning Vector Quantization | Zendy

A. Ayilara Oluwatobi | Zendy; O. Ajayi Anuoluwapo | Zendy; O. Jimoh Kudirat | Zendy

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A Synthetic Player for Ayὸ Board Game Using Alpha-Beta Search and Learning Vector Quantization

Author(s) -

A. Ayilara Oluwatobi,

O. Ajayi Anuoluwapo,

O. Jimoh Kudirat

Publication year - 2016

Publication title -

computer and information science

Language(s) - English

Resource type - Journals

eISSN - 1913-8997

pISSN - 1913-8989

DOI - 10.5539/cis.v9n3p1

Subject(s) - computer science , alpha (finance) , artificial intelligence , game tree , beta (programming language) , java , matlab , machine learning , multimedia , sequential game , operating system , game theory , programming language , mathematics , statistics , construct validity , mathematical economics , psychometrics

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.

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