TY - JFULL AU - Ahmad Sharieh and R Bremananth PY - 2011/1/ TI - A Robust Al-Hawalees Gaming Automation using Minimax and BPNN Decision T2 - International Journal of Computer and Information Engineering SP - 1613 EP - 1620 VL - 5 SN - 1307-6892 UR - https://publications.waset.org/pdf/14901 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 60, 2011 N2 - 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. ER -