Abstract
Research in two-player perfect information games has been one of the focuses of computer-game related studies in the domain of artificial intelligence. However, focus on an effective search program is insufficient to give the “taste” of actual entertainment in the gaming industry. Instead of focusing on effective search algorithm, we dedicate our study in realizing the possibility of applying strategy changing technique. However, quantifying and determining this possibility is the main challenge imposed in this study. For this purpose, the Conspiracy Number Search algorithm is considered where the maximum and minimum conspiracy numbers are recorded in the test bed of simple Tic-Tac-Toe and Othello game application. We analysed these numbers as the measures of critical position identifier which determines the right moment for possibility of applying strategy changing technique. For Tic-Tac-Toe game, the conspiracy numbers are analysed through operators formally defined in this article as \(\uparrow tactic\) and \(\downarrow tactic\) while variance of the conspiracy numbers are analysed in Othello game. Interesting results are obtained with convincing evidences but future works are still needed in order to further strengthen our hypothesis.
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References
van den Herik, H.J., Donkers, H., Spronck, P.H.: Opponent modelling and commercial games. In: Proceedings of the IEEE 2005 Symposium on Computational Intelligence and Games (CIG05), pp. 15–25 (2005)
Iida, H., Uiterwijk, J., van den Herik, H.J., Herschberg, I.: Potential applications of opponent-model search: Part 1. The domain of applicability. ICCA J. 16, 201–208 (1993)
Carmel, D., Markovitch, S.: Learning models of opponent’s strategy in game playing. Technion-Israel Institute of Technology, Center for Intelligent Systems (1993)
Iida, H., Uiterwijk, J., van den Herik, H.J., Herschberg, I.: Potential applications of opponent-model search. Part 2: risks and strategies. ICCA J. 17, 10–14 (1994)
Lorenz, U., Rottmann, V., Feldmann, R., Mysliwietz, P.: Controlled conspiracy number search. ICCA J. 18, 135–147 (1995)
Schaeffer, J., van den Herik, H.J.: Games, computers, and artificial intelligence. Chips Challenging Champions Games Comput. Artif. Intell. 134, 1–8 (2002)
Kishimoto, A., Winands, M.H., Müller, M., Saito, J.T.: Game-tree search using proof numbers: the first twenty years. ICGA J. 35, 131–156 (2012)
Jansen, P.: Using knowledge about the opponent in game-tree search. Ph.D. thesis, Carnegie Mellon University (1992)
Donkers, J.: Nosce Hostem: Searching with Opponent Models. Ph.D. thesis, Universiteit Maastricht (2003)
Ramon, J., Jacobs, N., Blockeel, H.: Opponent modeling by analysing play. In: Proceedings of Workshop on Agents in Computer Games. Citeseer (2002)
McAllester, D.A.: A new procedure for growing min-max trees. Technical report, Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA (1985)
McAllester, D.A.: Conspiracy numbers for min-max search. Artif. Intell. 35, 287–310 (1988)
Schaeffer, J.: Conspiracy numbers. Adv. Comput. Chess 5, 199–218 (1989)
Elkan, C.: Conspiracy numbers and caching for searching and/or trees and theorem-proving. In: IJCAI, pp. 341–348 (1989)
Schaeffer, J.: Conspiracy numbers. Artif. Intell. 43, 67–84 (1990)
van der Meulen, M.: Conspiracy-number search. ICCA J. 13, 3–14 (1990)
Allis, L.V., van der Meulen, M., van den Herik, H.J.: Proof-number search. Artif. Intell. 66, 91–124 (1994)
Neumann, L.J., Morgenstern, O.: Theory of Games and Economic Behavior, vol. 60. Princeton University Press Princeton, NJ (1947)
Shannon, C.E.: Xxii. programming a computer for playing chess. Philos. Mag. 41, 256–275 (1950)
McAllester, D.A., Yuret, D.: Alpha-beta-conspiracy search (1993)
Buro, M.: From simple features to sophisticated evaluation functions. In: van den Herik, H.J., Iida, H. (eds.) CG 1998. LNCS, vol. 1558, pp. 126–145. Springer, Heidelberg (1999)
Rosenbloom, P.S.: A world-championship-level othello program. Artif. Intell. 19, 279–320 (1982)
Landau, T.: Othello: Brief & basic. US Othello Association 920, pp. 22980–23425 (1985)
Ishitobi, T., Cincotti, A., Iida, H.: Shape-keeping technique and its application to checkmate problem composition. In: Ninth Artificial Intelligence and Interactive Digital Entertainment Conference (2013)
Acknowledgement
This research is funded by a grant from the Japan Society for the Promotion of Science, in the framework of the Grant-in-Aid for Challenging Exploratory Research (grant number26540189).
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Appendices
A Appendix
Experiment Design
This Othello study is conducted by the default opening position on 8\(\,\times \,\)8 board with 64 squares as shown in Fig. 7. Figure 8 denotes the significant squares that are given special names.
C-squares are adjacent to the corner D while X-squares are diagonally adjacent to the corner D. A-squares and B-squares are the edges of the board [23]. E-squares are adjacent to A-squares and B-squares. The evaluation function is defined as follows (Fig. 9):
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D-squares are good and high priority positions.
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C-squares and X-squares are bad positions which may give chances to the opponent player accessing corner D.
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A-squares are good positions if there exist no opponent’s discs at the adjacent squares.
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B-squares are bad positions where the opponent player might has chances to access to A-squares.
Table 8 shows the fitness values that have been assigned to each significant square on board.
B Appendix
Game Analysis using CN Variance and Position Scoring
*Applicable to all the figures
The trend line drawn in position scoring Figs. 10(c), 11(c), 12(c) and 13(c) is closely related to the CN\(_{\tiny \textit{variance}}\) trend line at Figs. 10(a), 11(a), 12(a) and 13(a) whereas the trend line drawn in position scoring Figs. 10(d), 11(d), 12(d) and 13(d) is closely related to the CN\(_{\tiny \textit{variance}}\) trend line at Figs. 10(b), 11(b), 12(b) and 13(b). CN\(_{\tiny \textit{variance}}\) trend line in Figs. 10(a), (b), 11(a), (b), 12(a), (b) and 13(a), (b) can be used to predict the trend of position scoring in Figs. 10(c), (d), 11(c), (d), 12(c), (d) and 13(c), (d).
Increasing CN\(_{\tiny \textit{variance}}\) trend line in Figs. 10(a), (b), 11(a), (b), 12(a), (b) and 13(a), (b) will lead to decreasing trend in position scoring which can be observed by Figs. 10(c), (d), 11(c), (d), 12(c), (d) and 13(c), (d). However, trend line observation is not applicable when there are infinity CN\(_{\tiny \textit{variance}}\) values.
C Appendix
Incorporating Strategy Changing for Black or White Player Only
*Applicable to all the figures
The trend line drawn in position scoring Figs. 14(c), 15(c), 16(c) and 17(c) is closely related to the CN\(_{\tiny \textit{variance}}\) trend line at Figs. 14(a), 15(a), 16(a) and 17(a) whereas the trend line drawn in position scoring Figs. 14(d), 15(d), 16(d) and 17(d) is closely related to the CN\(_{\tiny \textit{variance}}\) trend line at Figs. 14(b), 15(b), 16(b) and 17(b). CN\(_{\tiny \textit{variance}}\) trend line in Figs. 14(a), (b), 15(a), (b), 16(a), (b) and 17(a), (b) can be used to predict the trend of position scoring in Figs. 14(c), (d), 15(c), (d), 16(c), (d) and 17(c), (d).
Increasing CN\(_{\tiny \textit{variance}}\) trend line in Figs. 14(a), (b), 15(a), (b), 16(a), (b) and 17(a), (b) will lead to decreasing trend in position scoring which can be observed by Figs. 14(c), (d), 15(c), (d), 16(c), (d) and 17(c), (d). However, trend line observation is not applicable when there are infinity CN\(_{\tiny \textit{variance}}\) values.
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Khalid, M.N.A., Ang, E.M., Yusof, U.K., Iida, H., Ishitobi, T. (2015). Identifying Critical Positions Based on Conspiracy Numbers. In: Duval, B., van den Herik, J., Loiseau, S., Filipe, J. (eds) Agents and Artificial Intelligence. ICAART 2015. Lecture Notes in Computer Science(), vol 9494. Springer, Cham. https://doi.org/10.1007/978-3-319-27947-3_6
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