Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Discovering Effective Strategies for the Iterated Prisoner's Dilemma using Genetic Algorithms
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
How to playwell in non-zero sum games: some lessons from generalized traveler's dilemma
AMT'11 Proceedings of the 7th international conference on Active media technology
On Finding and Learning Effective Strategies for Complex Non-zero-sum Repeated Games
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Hi-index | 0.00 |
In game theory, the Traveler's Dilemma (abbreviated TD) is a non-zero-sum game in which two players attempt to maximize their own payoff without deliberately willing to damage the opponent. In the classical formulation of this problem, game theory predicts that, if both players are purely rational, they will always choose the strategy corresponding to the Nash equilibrium for the game. However, when played experimentally, most human players select much higher values (usually close to $100), deviating strongly from the Nash equilibrium and obtaining, on average, much higher rewards. In this paper we analyze the behaviour of a genetic algorithm that, by repeatedly playing the game, evolves the strategy in order to maximize the payoffs. In the algorithm, the population has no a priori knowledge about the game. The fitness function rewards the individuals who obtain high payoffs at the end of each game session. We demonstrate that, when it is possible to assign to each strategy a probability measure, then the search for good strategies can be effectively translated into a problem of search in a measure space using, for example, genetic algorithms. Furthermore, the codification of the genome as a probability distribution allows the analysis of common crossover and mutation operators in the uncommon case where the genome is a probability measure.