Abstract
In this research, we aim to create a computer player that gives fun to the opponent. Research on game AI has spread widely in recent years, and many games are being studied. Some of those studies have made remarkable results. Game research is aimed at strengthening computer players. However, it is unknown whether a computer player who is too strong is good. There may also be opponents who think that a computer player is not interesting if it is too strong. Therefore, we thought whether we could create a computer player who entertain the opponent while maintaining a certain degree of strength. To realize this idea, we use the Monte Carlo Tree Search. We tried to create a computer player that gives fun to the opponent by improving the Monte Carlo Tree Search. As a result of some experiments, we succeeded in giving fun, although it was a first step. On the other hand, many problems were found through experiments. In future, it is necessary to solve these problems.
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References
Coulom, R.: Monte-Carlo Tree Search in Crazy Stone. In: Proceedings of Game Programming Workshop 2007, pp. 74–75. Information Processing Society of Ja-pan, Japan (2007).
Ramanujan, R., Sabharwal, A., and Selman, B.: Understanding Sampling Style Adversarial Search Methods. In: Grünwald, P. and Spirtes, P. (eds.) Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, pp. 474–483. AUAI Press (2010).
Nijssen, J. P. A. M. and Winands, M. H. M.: Enhancements for Multi-Player Monte-Carlo Tree Search. In: van den Herik, H. Jaap, Hiroyuki, I. and Aske, P. (eds.) 7th International Conference on Computers and Games, vol. 6515, pp. 238–249. Springer, Japan (2010).
Arneson, B., Hayward, R. B. and P. Henderson: Monte Carlo Tree Search in Hex. IEEE Transactions on Computational Intelligence and AI in Games 2(4), 251–258 (2010).
AlphaGo: Mastering the ancient game of Go with Machine Learning, https://research.googleblog.com/2016/01/alphago-mastering-ancient-game-of-go.html last accessed 2017/06/26.
Choi, D., Kim, H., Kim, J.: Toward the construction of fun computer games: Dif-ferences in the views of developers and players. Personal Technologies 3(3), 92-104 (1999).
Iida, H., Takeshita, N. and Yoshimura, J.: A metric for entertainment of boardgames: its implication for evolution of chess variants. In: Nakatsu, R. and Hoshino, J. (eds.) IWEC2002 Proceedings, pp. 65-72. Kluwer (2003).
Auer, P., Cesa-Bianchi, N. and Fischer, P.: Finite-time Analysis of the Multiarmed Bandit Problem. Machine Learning 47(2), 235–256 (2002).
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Takaoka, Y., Kawakami, T., Ooe, R. (2018). A Fundamental Study of a Computer Player Giving Fun To the Opponent: Targeting Hanafuda, a Card Game in Japan. In: Lokman, A., Yamanaka, T., Lévy, P., Chen, K., Koyama, S. (eds) Proceedings of the 7th International Conference on Kansei Engineering and Emotion Research 2018. KEER 2018. Advances in Intelligent Systems and Computing, vol 739. Springer, Singapore. https://doi.org/10.1007/978-981-10-8612-0_46
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DOI: https://doi.org/10.1007/978-981-10-8612-0_46
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