Exploiting focal points among alternative solutions: Two approaches
Annals of Mathematics and Artificial Intelligence
Helping based on future expectations
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Learning to compete, compromise, and cooperate in repeated general-sum games
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning in one-shot strategic form games
ECML'06 Proceedings of the 17th European conference on Machine Learning
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Friend or foe?: detecting an opponent's attitude in normal form games
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Cooperation between self-interested agents in normal form games
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
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We describe a simple environment to study cooperation between two agents and a method of achieving cooperation in that environment. The environment consists of randomly generated normal form games with uniformly distributed pay-offs. Agents play multiple games against each other, each game drawn independently from the random distribution. In this environment cooperation is difficult. Tit-for-Tat cannot be used because moves are not labeled as "cooperate" or "defect", fictitious play cannot be used because the agent never sees the same game twice, and approaches suitable for stochastic games cannot be used because the set of states is not finite. Our agent identifies cooperative moves by assigning an attitude to its opponent and to itself. The attitude determines how much a player values its opponents payoff, i.e how much the player is willing to deviate from strictly selfinterested behavior. To cooperate, our agent estimates the attitude of its opponent by observing its moves and reciprocates by setting its own attitude accordingly. We show how the opponent's attitude can be estimated using a particle filter, even when the opponent is changing its attitude.