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The Surakarta Bot Revealed

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 614)

Abstract

The board game Surakarta has been played at the ICGA Computer Olympiad since 2007. In this paper the ideas behind the agent SIA, which won the competition five times, are revealed. The paper describes its \(\alpha \beta \)-based variable-depth search mechanism. Search enhancements such as multi-cut forward pruning and Realization Probability Search are shown to improve the agent considerably. Additionally, features of the static evaluation function are presented. Experimental results indicate that features, which reward distribution of the pieces and penalize pieces that clutter together, give a genuine improvement in the playing strength.

Keywords

Evaluation Function Leaf Node Capture Move Board Game Realization Probability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Special thanks go to the anonymous referees whose comments helped to improve this paper.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Games and AI Group, Department of Data Science and Knowledge EngineeringMaastricht UniversityMaastrichtThe Netherlands

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