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A Robust Al-Hawalees Gaming Automation using Minimax and BPNN Decision

Authors: Ahmad Sharieh, R Bremananth

Abstract:

Artificial Intelligence based gaming is an interesting topic in the state-of-art technology. This paper presents an automation of a tradition Omani game, called Al-Hawalees. Its related issues are resolved and implemented using artificial intelligence approach. An AI approach called mini-max procedure is incorporated to make a diverse budges of the on-line gaming. If number of moves increase, time complexity will be increased in terms of propositionally. In order to tackle the time and space complexities, we have employed a back propagation neural network (BPNN) to train in off-line to make a decision for resources required to fulfill the automation of the game. We have utilized Leverberg- Marquardt training in order to get the rapid response during the gaming. A set of optimal moves is determined by the on-line back propagation training fashioned with alpha-beta pruning. The results and analyses reveal that the proposed scheme will be easily incorporated in the on-line scenario with one player against the system.

Keywords: Artificial Neural Network, back propagation gaming, Leverberg-Marquardt, minimax procedure

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1083837

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References:


[1] Boykin,. S., Neural A Comprehensive Foundation, Prentice Hall, 2008.
[2] Bowling, M., Johanson, M., Burch. N., and Szafron B, "Strategy Evaluation in Extensive game, with Importance Sampling", ICML-08, 2008.
[3] Bryant, B. B. and Makulainen, R., "Acquiring visibly Intelligent behavior with example-guided nouroevolaticn" , AAAI 07, 2008.
[4] Bylander, T., "Complexity Results for Serial Decomposability", Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI-92), San Jose, California, AAAI Press, pp. 729-734, 1992.
[5] Dechter. R. and Frost, D., "Backjump-based Backtracking for Constraint Satisfaction Problems", Journal of Artificial Intelligence, 136(2), 147- 188, 2002.
[6] Ginsberg, N., "Imperfect Information in A Computationally Challenging Game", IJAIR, 14, 302-358, 2001.
[7] Kierulf, A., Chen, K., and Nievergelt, J., "Smart Game Board and Go Explorer: A study in Software and Knowledge Engineering", Communications of the Association for Computing Machinery, 33(2): 152-167, 1990.
[8] Kumar, V., Nau, D. S., and Kanal, L. N., "A General Branch-and-Bound Formulation for AND/OR Graph and Game Tree Search"., In Kanal, L. N. and Kumar, V., editors, Search in Artificial Intelligence, chapter 3, pages 91-130. Springer-Verlag, Berlin, 1988.
[9] Stuart Russell, Peter Norvig, Artificial Intelligence A Modern Approach, Pearson Education, 2010.