Title :
Optimal Strategies of the Iterated Prisoner´s Dilemma Problem for Multiple Conflicting Objectives
Author :
Mittal, Shashi ; Deb, Kalyanmoy
Author_Institution :
Oper. Res. Center, Massachusetts Inst. of Technol., Cambridge, MA
fDate :
6/1/2009 12:00:00 AM
Abstract :
In this paper, we present a new paradigm of searching optimal strategies in the game of iterated prisoner´s dilemma (IPD) using multiple-objective evolutionary algorithms. This method is more useful than the existing approaches, because it not only produces strategies that perform better in the iterated game but also finds a family of nondominated strategies, which can be analyzed to decipher properties a strategy should have to win the game in a more satisfactory manner. We present the results obtained by this new method and discuss sub-strategies found to be common among nondominated strategies. The multiobjective treatment of the IPD problem demonstrated here can be applied to other similar game-playing tasks.
Keywords :
evolutionary computation; game theory; iterative methods; IPD problem; game-playing tasks; iterated game; iterated prisoner dilemma problem; multiple conflicting objectives; multiple-objective evolutionary algorithms; optimal strategies; Biology; Evolution (biology); Evolutionary computation; Game theory; Helium; Machine learning; Nash equilibrium; Operations research; Performance analysis; Steady-state; Evolutionary algorithms; games; multiobjective optimization; prisoner´s dilemma;
Journal_Title :
Evolutionary Computation, IEEE Transactions on
DOI :
10.1109/TEVC.2008.2009459