Technical Note: \cal Q-Learning
Machine Learning
Rules of encounter: designing conventions for automated negotiation among computers
Rules of encounter: designing conventions for automated negotiation among computers
Game Theory and Decision Theory in Multi-Agent Systems
Autonomous Agents and Multi-Agent Systems
Friend-or-Foe Q-learning in General-Sum Games
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
An Introduction to MultiAgent Systems
An Introduction to MultiAgent Systems
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
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
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We are interested in two-person games whose structure is far from zero-sum. We study the iterated Traveler's Dilemma (TD) which is a two-player, non-zero sum game that, depending on the exact values of its critical parameters, may offer plenty of incentives for cooperation. We first briefly summarize the results of a round-robin tournament with 36 competing strategies that was motivated by the work by Axelrod et al. on the iterated Prisoner's Dilemma. We then generalize the "default" version of Iterated TD with respect to two important game parameters, the bonus value and the "granularity" of the allowable bids. We analytically show the impact of the ratio of these two parameters on the game structure. Third, we re-run the 36-player round-robin tournament and investigate how varying the bonus-to-granularity ratio affects relative performances of various types of strategies in the tournament. We draw some conclusions based on those results and outline some promising ways forward in further investigating games whose structures seem to defy the prescriptions of classical game theory.