Complex Information Game Problem Based on Artificial Neural Network
Xingfeng Liu, Tiansong Zhou, Zhongxia Zheng
Available Online July 2015.
- https://doi.org/10.2991/lemcs-15.2015.34How to use a DOI?
- Artificial intelligence; Artificial neural network; Complex information; Game theory
- The assumption of game theory is that the players in game must be rational. In the game of incomplete information, participants are not completely clear about the game. Therefore, usually there is a probability distribution of strategy selection in game. It is very complicated to know the real game information of the social and economic problems. In fact, the actual situation for many problems is that game players are irrational, or the probability distribution of game players’ strategies cannot be gotten, even the strategy sets are not complete (infinite strategy sets).There are many limitations in application of the traditional game theory. In this paper, the concept of complex information game and its Nash equilibrium are presented. It is proved that the complex information game problem can be solved by artificial neural network. An example on how to solve the complex information game problem with artificial neural network is given as well. Researchers hope that more and more scholars can use artificial intelligence theory to analyze the game theory problem. Therefore, the complex information game problems can be dealt more efficiently.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Xingfeng Liu AU - Tiansong Zhou AU - Zhongxia Zheng PY - 2015/07 DA - 2015/07 TI - Complex Information Game Problem Based on Artificial Neural Network PB - Atlantis Press SP - 171 EP - 176 SN - 1951-6851 UR - https://doi.org/10.2991/lemcs-15.2015.34 DO - https://doi.org/10.2991/lemcs-15.2015.34 ID - Liu2015/07 ER -