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Title: | Review of Kalah Game research and the proposition of a novel heuristic-deterministic algorithm compared to tree-search solutions and human decision-making |
Author: | Pekař, Libor; Matušů, Radek; Andrla, Jiří; Litschmannová, Martina |
Document type: | Review (English) |
Source document: | Informatics. 2020, vol. 7, issue 3 |
ISSN: | 2227-9709 (Sherpa/RoMEO, JCR) |
DOI: | https://doi.org/10.3390/INFORMATICS7030034 |
Abstract: | The Kalah game represents the most popular version of probably the oldest board game ever-the Mancala game. From this viewpoint, the art of playing Kalah can contribute to cultural heritage. This paper primarily focuses on a review of Kalah history and on a survey of research made so far for solving and analyzing the Kalah game (and some other related Mancala games). This review concludes that even if strong in-depth tree-search solutions for some types of the game were already published, it is still reasonable to develop less time-consumptive and computationally-demanding playing algorithms and their strategies Therefore, the paper also presents an original heuristic algorithm based on particular deterministic strategies arising from the analysis of the game rules. Standard and modified mini-max tree-search algorithms are introduced as well. A simple C++ application with Qt framework is developed to perform the algorithm verification and comparative experiments. Two sets of benchmark tests are made; namely, a tournament where a mid-experienced amateur human player competes with the three algorithms is introduced first. Then, a round-robin tournament of all the algorithms is presented. It can be deduced that the proposed heuristic algorithm has comparable success to the human player and to low-depth tree-search solutions. Moreover, multiple-case experiments proved that the opening move has a decisive impact on winning or losing. Namely, if the computer plays first, the human opponent cannot beat it. Contrariwise, if it starts to play second, using the heuristic algorithm, it nearly always loses. © 2020 by the authors. |
Full text: | https://www.mdpi.com/2227-9709/7/3/34 |
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