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
Complete Classes of Strategies for the Classical Iterated Prisoner's Dilemma
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Iterated regret minimization: a new solution concept
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
An Introduction to MultiAgent Systems
An Introduction to MultiAgent Systems
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
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
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We study complex non-zero-sum iterated two player games, more specifically, various strategies and their performances in iterated travelerâ脗聙脗聶s dilemma (ITD). We focus on the relative performances of several types of parameterized strategies, where each such strategy type corresponds to a particular â脗聙脗聹philosophyâ脗聙脗聺 on how to best predict opponentâ脗聙脗聶sfuture behavior and/or entice the opponent to alter its behavior. We are particularly interested in adaptable, learning and/or evolving strategies that try to predict the future behavior of the other player, and hence optimize their own behavior in the long run. We also study strategies that strive to minimize risk, as risk minimization has been recently suggested to be the appropriate solution paradigm for ITD and several other complex games that have posed difficulties to classical game theory. We share the key insights from an elaborate round-robin tournament that we have implemented and analyzed. We draw some conclusions on what kinds of adaptability and models of the other playerâ脗聙脗聶s behavior seem to be most effective in the long run. Lastly, we indicate some promising ways forward toward a better understanding of learning how to play complex iterated games well.