Title

Neural Networks and Markov Models for the Iterated Prisoner's Dilemma

Abstract

The study of strategic interaction among a society of agents is often handled using the machinery of game theory. This research examines how a Markov Decision Process (MDP) model may be applied to an important element of repeated game theory: the iterated prisoner's dilemma. Our study uses a Markovian approach to the game to represent the problem of in a computer simulation environment. A pure Markov approach is used on a simplified version of the iterated game and then we formulate the general game as a partially observable Markov decision process (POMDP). Finally, we use a cellular structure as an environment for players to compete and adapt. We apply both a simple replacement strategy and a cellular neural network to the environment.

Meeting Name

2009 International Joint Conference on Neural Networks, IJCNN '09 (2009: Jun. 14-19, Atlanta, GA)

Department(s)

Electrical and Computer Engineering

International Standard Book Number (ISBN)

9781424435531

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2009 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.


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