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Title : Satisficing in Multiagent Learning

Learning in the presence of multiple, possibly antagonistic adaptive agents presents special challenges to algorithm designers, especially in environments with limited information. Desirable properties of such algorithms include (a) security, which requires agents to avoid exploitation by antagonistic agents, and (b) efficiency, which requires agents to find nearly pareto efficient solutions when in the presence of associates who are inclined to coordinate. In this paper, we present and analyze a variation of Karandikar et al.’s learning algorithm [20] that is guaranteed to be secure and which is efficient in self-play in a very large set of matrix games. The algorithm requires that the agent knows its own set of actions and observes its own payoffs, but it does not require knowledge of the actions or payoffs of the agents with which it associates. Consequently, the algorithm is appropriate for minimal information environments.