Abstract
Strategy is one of the most important factors to win a game. Especially in zero-sum game, where a loser is necessary to make a winner, the player who has better strategy can be the winner. A fixed or solid strategy cannot be the better strategy, because game is like dancing with partner and responding the partner’s behavior is important. In order to win, the strategy should be dynamically adapted to the situation of the game according to the opponent’s action and at the same time, the strategy should provide the suitable action with performance limitation such as time and space. In this paper, we propose a method of dynamically modifying the strategy to the drift of the game. This method classifies the game situation and selects the best action in that situation by evaluating all the possible options.
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References
Sun T (2005) Translated by Lionel Giles [Translation first published 1910]: the art of war by Sun Tzu—Special Edition. El Paso Norte Press. ISBN 0-9760726-9-6
Xiang L, Guo Y, Lan T (2007) Topological cluster: a generalized view for density-based spatial clustering, international conference on management science and engineering pp 422–428
Pelleg D, Moore AW (2000) Extending K-means with efficient estimation of the number of clusters, ICML 2000 proceedings of the seventeenth international conference on machine learning. ISBN:1-55860-707-2
Kaelbling LP, Littman ML, Moore AW (1996) Reinforcement learning: a survey. J Artif Intell Res 4:237–285
Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. MIT Press, Cambridge, ISBN 0-262-19398-1
Othello game. http://en.wikipedia.org/wiki/Reversi
US Othello Association. http://www.usothello.org/joomla/
Russell SJ, Norvig P (2003) Artificial intelligence: a modern approach, 2nd edn. Prentice Hall, Upper Saddle River, pp 163–171. ISBN 0-13-790395-2
Victor A (1994) Searching for solutions in games and artificial intelligence. PhD Thesis, University of Limburg, Maastricht, The Netherlands. ISBN 9090074880
Heineman GT, Gary P, Stanley S (2008) Chapter 7: path finding in AI. Algorithms in a Nutshell. O'Reilly Media, Sebastopol, pp 217–223. ISBN 978-0-596-51624-6
Reilly DL, Cooper LN, Elbaum C (1982) A neural model for category learning. Biol Cybern 45:35–41. doi:10.1007/BF00387211
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Lee, K., Rho, S., Kim, M. (2011). Self-Adaptive Strategy for Zero-Sum Game. In: Park, J., Arabnia, H., Chang, HB., Shon, T. (eds) IT Convergence and Services. Lecture Notes in Electrical Engineering, vol 107. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2598-0_19
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DOI: https://doi.org/10.1007/978-94-007-2598-0_19
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