Abstract
In this study, we construct a non-human agent that can play the werewolf game (i.e., AI wolf) with aims of creating more advanced intelligence and acquire more advanced communication skills for AI-based systems. We therefore constructed a behavioral model using information regarding human players and the decisions made by such players; all such information was obtained from play logs of the werewolf game. To confirm our model, we conducted simulation experiments of the werewolf game using an agent based on our proposed behavioral model, as well as a random agent for comparison. Consequently, we obtained an 81.55% coincidence ratio of agent behavior versus human behavior.
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Acknowledgements
We heartily thank Mr. Ninjin for allowing us to use the data of the Werewolf BBS. This study received a grant of JSPS Grants-in-aid for Scientific Research 15K12180.
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Hirata, Y. et al. (2016). Werewolf Game Modeling Using Action Probabilities Based on Play Log Analysis. In: Plaat, A., Kosters, W., van den Herik, J. (eds) Computers and Games. CG 2016. Lecture Notes in Computer Science(), vol 10068. Springer, Cham. https://doi.org/10.1007/978-3-319-50935-8_10
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DOI: https://doi.org/10.1007/978-3-319-50935-8_10
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