Critical Analysis of Decision Making Experience with a Machine Learning Approach in Playing Ayo Game

The major goal in defining and examining game scenarios is to find good strategies as solutions to the game. A plausible solution is a recommendation to the players on how to play the game, which is represented as strategies guided by the various choices available to the players. These choices invariably compel the players (decision makers) to execute an action following some conscious tactics. In this paper, we proposed a refinement-based heuristic as a machine learning technique for human-like decision making in playing Ayo game. The result showed that our machine learning technique is more adaptable and more responsive in making decision than human intelligence. The technique has the advantage that a search is astutely conducted in a shallow horizon game tree. Our simulation was tested against Awale shareware and an appealing result was obtained.

Authors:

• Ibidapo O. Akinyemi
• Ezekiel F. Adebiyi
• Harrison O. D. Longe

Keywords:

• Decision making
• Machine learning
• Strategy
• Ayo game.

References:

[1] O. A. Adedayo, Optimal Strategies in Two-Person Zero-Sum Games. A
Seminar paper presented at Covenant University, Ota, Nigeria. May 25,
2005
[2] M. O. Ajayi, The Soccer Pitch and the Arena of Politics in Nigeria. A
Paper Presented at the Public Lecture Delivered at Covenant University,
Ota, Nigeria, on 27th February, 2007.
[3] D. .M. Kreeps, Game Theory and Economic Modeling, Oxford
Unversity Press, 1990.
[4] H.J.R. Murray, A History of Board Games other than Chess. Clarendon
Press, Oxford, (1952..
[5] A. O. Odeleye, "Ayo": A popular Yoruba game, Oxford University
Press, Ibadan, Nigeria, 1977.B. Smith, "An approach to graphs of linear
forms (Unpublished work style)," unpublished.
[6] E. F. Adebiyi, A step further toward Improving "Ayo" Game System.
AMSE, 3(2);, pp.53- 69, 1999.
[7] T. O. Adewoye on Certain Combinatorial Number Theoretic Aspects of
the African Game of "Ayo". AMSE REVIEW, vol. 14, no, 2, pp. 41-63,
1990.
[8] O. O. Olugbara, T. O. Adewoye, and I. O. Akinyemi, An Investigation
of Miinimax Search Techniques for Evolving Ayo/Awari Player.
Accepted for Publication, in IEEE - ICICT Conference Proceedings, 10
- 12 December 2006
[9] D.M. Broline, and D.E. Loeb, The Combinatorics of Mancala-type
games: Ayo, Tchoukaillon and 1/¤Ç. UMAP, J., 10(1), 1995.
[10] A. L. Samuel, Some Studies in Machine Learning Using the Game of
Checkers, IBM J. of Res. And Dev. 3, 210-229, 1959.
[11] A.L. Samuel, Some Studies in Machine Learning Using the Game of
Checkers II- recent Progress, IBM J. of Res. And Dev. 11(6), 601 - 617,
1967.
[12] M. Bowling, J. Furukranz, T. Graepel, and R. Musick, Machine
Learning and Games. Springer Science plus Business Media, LLC, Mach
Learn (2006), m vol. 63, pp 211 - 215.
[13] M. Henk, learning to play chess unsing Reinforement Learniing with
Database Games. M.Sc. Thesis, Cognitive Artifiial Intelligene, Utreht
universty, 2003.
[14] J. Jantzen, Introduction to Perceptron Networks. Technical Report No
98-H873, Department of Automation, Technical University of Denmark,
1998.
[15] V. L. kristof, Basic Statistics and Metrics for Sensor Analysis, 2004.
Available online -
http://ubicomp.jancs.ac.uk/~kristof/research/notes/basicstats/index.html.
Downloaded 20/10/2007.