Complex Information Game Problem Based on Artificial Neural Network

DOI

10.2991/lemcs-15.2015.34How to use a DOI?

Keywords

Artificial intelligence; Artificial neural network; Complex information; Game theory

Abstract

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.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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
BT  - Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science
PB  - Atlantis Press
SP  - 171
EP  - 176
SN  - 1951-6851
UR  - https://doi.org/10.2991/lemcs-15.2015.34
DO  - 10.2991/lemcs-15.2015.34
ID  - Liu2015/07
ER  -