TOURNAMENT-WINNING STRATEGY FOR ITERATED OPTIONAL PRISONER'S DILEMMA

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Authors
Shamma, Ahmed A.
Subjects
game theory
iterated optional prisoner's dilemma
IOPD
Advisors
Kroll, Joshua A.
Date of Issue
2000-09
Date
Sep-20
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Iterated optional prisoner's dilemma (IOPD) is an adversarial game that can be used to model several real-world scenarios, from mutual grooming between primates to alliances between business firms. This study utilizes simulation techniques to determine winning strategies for IOPD tournaments in a variety of initial conditions. Machine learning techniques are used to iteratively improve upon the winning strategy, culminating in a single undefeated strategy. The outcome of this study is a single strategy that we claim is likely to win an IOPD tournament for most reasonable initial conditions.
Type
Thesis
Description
Includes supplementary material
URI
https://hdl.handle.net/10945/66137
Series/Report No
Department
Computer Science (CS)
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Distribution Statement
Approved for public release. distribution is unlimited
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States
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