Abstract   In this paper, we present a new technique of repres enting the player’s strategies by adaptive automata, which can handle complex strategies in large populations effe ctively. The representation the player’s strategies have a great impact on changing the player’s behaviour in rational environ ments. This model is built on the basis of changing the behaviour of the player’s gradually toward the cooperation. The grad ualism is achieved by constructing three different adaptive automata at three different levels. The results showed that our model could represent the player’s strategies effi ciently. The results proofed that the model is able to enhance the cooperation l evel between the participated player’s through few tournaments.

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[18] Zhang J., Adaptive Learning via Selectionism and Bayesianism. Part I: Connection between the Two, Neural Networks , vol. 22, no. 3, pp. 2203 228, 2009. Sally Almanasra is a PhD student at the school of Computer Sciences at Universiti Sains Malaysia. In 2007, she obtained her Master degree in computer science from AL3Balqa Applied University. Currently, she is working in the field of game theory and evolving systems. Khaled Suwais received his BSc degree in computer science from Al3 Bayt University, Jordan in 2004, MSc and PhD degrees in computer science from University Sains Malaysia, Malaysia in 2005 and 2009, respectively. Currently, he is an assistant professor at the faculty of computer s tudies at Arab Open University, Riyadh. His research inter est includes: cryptography, information security, paral lel computing and game theory. Muhammad Rafie received BA in business studies degree from Macalester College, St Paul, Minnesota, USA in 1985 and MBA in management information system from University of Dallas, Texas, USA in 1987. Currently, he is an associate professor at the School of Computer Sciences, Universiti Sains Malaysia, Penang. His research interest includes e3learning, mobile learn ing, computer games, virtual reality, and RFID.