Modeling and Analysis Iterated Prison Dilemma Game by Grossberg Counter-Propagation Neural Network
Subject Areas : electrical and computer engineering
Gh. A.
Montazer
1
(Tarbiat Modares University)
N.
Rastegar Ramshe
2
Alireza
Askarzadeh
3
(دانشگاه تحصیلات تکمیلی صنعتی و فنّاوری پیشرفته کرمان)
Abstract :
Most of the time effective decisions in strategic situations such as competitive issues require a non-linear mapping between stimulus and response. Artificial neural networks can be an appropriate way for modeling and solving these kinds of problems. Prison Dilemma Game is a well-known game that is proposed in game theory. This paper tries to describe how using neural network, the iterated prisoner’s dilemma game can be modeled and analyzed. To do this a Grossberg Counter-Propagation Neural Network (GCP-NN) has been designed to play this game. Results show the capability of this method in complete modeling game. The results present the efficiency of the new method in comparison with the two conventional methods: Tit For Tat (TFT) strategy and Perceptron modeled game.
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