Abstract:In this paper, a Game system with two players is given. Each Player is an agent, and the input of one player is the output of another. The behavior of each player is described by the neural networks so as to make it like that of the humans as much as possible. It is adjusted with the change of neural network weights according to its cost function. Furthermore, 15 kinds of expert decision based on the psychology decision behavior of human being are used in the game system as a decision mechanism for the first time. Some simulations are carried out by using examples from reference [10]. The results indicate the validity of the proposed method which is based on neural networks and the psychology decision behavior of human being.
吕柏权,曹媛. 基于神经元网络的博弈行为研究*[J]. 模式识别与人工智能, 2007, 20(1): 21-27.
Lü BaiQuan, CAO Yuan. Study of the Behavior of Game System Based on Neural Networks. , 2007, 20(1): 21-27.
[1] Basar T, Srikant R. Network Game with a Large Number of Followers. Journal of Optimization Theory and Applications, 2002, 115(3): 479490 [2] Fernandez F R, Hinojosa M A, Puerto J. Core Solutions in VectorValued Games. Journal of Optimization Theory and Applications, 2002, 112(2): 331360 [3] Molina E, Tejada J. Linear Production Games with Committee Control: Limiting Behaviour of the Core. European Journal of Operational Research, 2004, 154(3): 609625 [4] Wang H, Guo M, Efstathiou J. A GameTheoretical Cooperative Mechanism Design for a TwoEchelon Decentralized Supply Chain. European Journal of Operational Research, 2004, 157(2): 372388 [5] Dockner E J, Leitmann G. Coordinate Transformations and Derivation of OpenLoop Nash Equilibria. Journal of Optimization Theory and Applications, 2001, 110(1): 115 [6] Liu Y, Simann M A. Noninferior Nash Strategies for MultiTeam Systems. Journal of Optimization Theory and Applications, 2004, 120(1): 2951 [7] Souza G C. Product Introduction Decisions in a Duopoly. European Journal of Operational Research, 2004, 152(3): 745757 [8] Fatima S S, Wooldridge M J, Jennings N R. An AgendaBased Framework for MultiIssue Negotiation. Artificial Intelligence, 2004, 152(1): 145 [9] Komarova N, Niyogi P. Optimizing the Mutual Intelligibility of Linguistic Agents in a Shared World. Artificial Intelligence, 2004, 154(1/2): 142 [10]Vincent T L, Goh B S, Teo K L. TrajectoryFollowing Algorithms for MinMax Optimization Problems. Journal of Optimization Theory and Applications, 1992, 75(3): 501519