Title: Artificial intelligence based on MCDM for the board game of the Royal Game of Ur

Authors: Tomáš Roskovec; Petr Chládek; Daniel Hejplík; Štĕpán Mudra; Marek Šulista

Addresses: Department of Applied Mathematics and Informatics, Faculty of Economics, The University of South Bohemia in České Budějovice, České Budějovice, Czech Republic ' Department of Applied Mathematics and Informatics, Faculty of Economics, The University of South Bohemia in České Budějovice, České Budějovice, Czech Republic ' Department of Applied Mathematics and Informatics, Faculty of Economics, The University of South Bohemia in České Budějovice, České Budějovice, Czech Republic ' Department of Applied Mathematics and Informatics, Faculty of Economics, The University of South Bohemia in České Budějovice, České Budějovice, Czech Republic ' Department of Applied Mathematics and Informatics, Faculty of Economics, The University of South Bohemia in České Budějovice, České Budějovice, Czech Republic

Abstract: The Royal Game of Ur is an ancient board game with random elements and strategies. We introduce two methods of designing simple but effective artificial intelligence (AI) that performs well in this game against both human players and chosen AI available online. We present both the description of the development of AI and the performance results. The advantage and originality of the method is the easy evaluation of a player's move based in such a way that a simple program or human player may follow the strategy as a guideline. The multiple-criteria decision-making methods in use are the lexicographic semi-order method and the weighted sum method. The weights and ordering of criteria are set by automatic testing software based on an evolutionary algorithm for searching the optimum.

Keywords: artificial intelligence; AI; board games; multiple-criteria decision-making; MCDM; weighted sum method; WSM; lexicographic semi-order method; LSM; evolutionary algorithm; Royal Game of Ur; RGoU.

DOI: 10.1504/IJADS.2023.133138

International Journal of Applied Decision Sciences, 2023 Vol.16 No.5, pp.545 - 564

Received: 19 Aug 2021
Accepted: 01 Feb 2022
Published online: 01 Sep 2023 *

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