Abstract
Among many categories, board games can be classified into two main categories: Games with perfect information and games with imperfect information. The first category can be represented by the example of “Chess” game where the information about the board is open to both players. The second category can be determined with the “Ghosts” game. Players can see the position of the opponent’s pieces on the board whereas the identity of the ghost pieces (good or bad) is hidden, which makes this game uncertain to apply search state space based technique. In this work, we have investigated the opponent game state with uncertainty for Ghosts using machine learning algorithms. From last year competition replay data, we extracted several features and apply various machine learning algorithms to infer game state. Also, we compare our experimental results to the previous prototype based approach. As a result, our proposed method shows more accurate results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Campbell, M., Hoane Jr., A.J., Hsu, F.: Deep Blue. Artif. Intell. 134, 57–83 (2002)
Schaeffer, J., Burch, N., Björnsson, Y., Kishimoto, A., Müller, M., Lake, R., Lu, P., Sutphen, S.: Checkers is solved. Science 317, 1518–1522 (2007)
Weber, B.G., Mateas, M.: A data mining approach to strategy prediction. In: IEEE Symposium on Computational Intelligence and Games (CIG), pp. 140–147 (2009)
Cho, H.-C., Kim, K.-J., Cho, S.-B.: Replay-based strategy prediction and build order adaptation for StarCraft AI bots. In: 2013 IEEE Conference on Computational Intelligence in Games (CIG), pp. 1–7 (2013)
Aiolli, F., Palazzi, C.E.: Enhancing artificial intelligence in games by learning the opponent’s playing style. In: Ciancarini, P., Nakatsu, R., Rauterberg, M., Roccetti, M. (eds.) New Frontiers for Entertainment Computing. IFIP, vol. 279, pp. 1–10. Springer, Boston (2008)
Aiolli, F., Palazzi, C.E.: Enhancing artificial intelligence on a real mobile game. International Journal of Computer Games Technology, Article ID 456169 (2009)
Hsieh, J.-L., Sun, C.-T.: Building a player strategy model by analyzing replays of real-time strategy games. In: IEEE International Joint Conference on Neural Networks (IJCNN), pp. 3106–3111 (2008)
Han, A., Zhuang, Q., Han, F.: A strategy based on probability theory for poker game. In: IET International Conference on Information Science and Control Engineering, pp. 1–5 (2012)
Ponsen, M., Gerritsen, G., Chaslot, G.: Integrating opponent models with Monte-Carlo tree search in Poker. In: Workshops at the Twenty-Fourth AAAI Conference on Artificial Intelligence (2010)
Ghosts Challenge (2013), https://ghosts-challenge.math.unipd.it/2013/
Brief Description, Team “BLISS”, https://ghosts-challenge.math.unipd.it/public/docs/2013/bliss.pdf
Geister Implementation Strategy, Team, MU Tigers, https://ghosts-challenge.math.unipd.it/public/docs/2013/mutigers.pdf
Buck, A., Banerjee, T., Keller, J.: Evolving a fuzzy goal-driven strategy for the game of geister. In: IEEE International Congress on Evolutionary Computation (CEC) (July 2014)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explor. Newsl. 11, 10–18 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Farooq, S.S., Park, H., Kim, KJ. (2015). Inference of Opponent’s Uncertain States in Ghosts Game Using Machine Learning. In: Handa, H., Ishibuchi, H., Ong, YS., Tan, KC. (eds) Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems - Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-13356-0_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-13356-0_27
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13355-3
Online ISBN: 978-3-319-13356-0
eBook Packages: EngineeringEngineering (R0)