Learning branches and learning to win closed games
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
A competitive approach to game learning
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Exactly Learning Automata of Small Cover Time
Machine Learning - Special issue on the eighth annual conference on computational learning theory, (COLT '95)
The power of a pebble: exploring and mapping directed graphs
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Exploration Strategies for Model-based Learning in Multi-agent Systems: Exploration Strategies
Autonomous Agents and Multi-Agent Systems
The power of a pebble: exploring and mapping directed graphs
Information and Computation
Stable repeated strategies for information exchange between two autonomous agents
Artificial Intelligence
Learning and Exploiting Relative Weaknesses of Opponent Agents
Autonomous Agents and Multi-Agent Systems
Efficient learning of multi-step best response
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Playing games in many possible worlds
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Stable strategies for sharing information among agents
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Multiagent learning for open systems: a study in opponent classification
Adaptive agents and multi-agent systems
Nash convergence of gradient dynamics in general-sum games
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
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We examine the problem of learning to play various games optimally against resource-bounded adversaries, with an explicit emphasis on the computational efficiency of the learning algorithm. We are especially interested in providing efficient algorithms for games other than penny-matching (in which payoff is received for matching the adversary's action in the current round), and for adversaries other than the classically studied finite automata. In particular, we examine games and adversaries for which the learning algorithm's past actions may strongly affect the adversary's future willingness to "cooperate" (that is, permit high payoff), and therefore require carefully planned actions on the part of the learning algorithm. For example, in the game we call contract, both sides play O or 1 on each round, but our side receives payoff only if we play 1 in synchrony with the adversary; unlike penny-matching, playing O in synchrony with the adversary pays nothing. The name of the game is derived from the example of signing a contract, which becomes valid only if both parties sign (play 1).