@article{(Open Science Index):https://publications.waset.org/pdf/14901,
	  title     = {A Robust Al-Hawalees Gaming Automation using Minimax and BPNN Decision},
	  author    = {Ahmad Sharieh and  R Bremananth},
	  country	= {},
	  institution	= {},
	  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.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {5},
	  number    = {12},
	  year      = {2011},
	  pages     = {1614 - 1620},
	  ee        = {https://publications.waset.org/pdf/14901},
	  url   	= {https://publications.waset.org/vol/60},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 60, 2011},
	}