Towards Social Complexity Reduction in Multiagent Learning: The ADHOC Approach

Michael Rovatsos and Marco Wolf

This paper presents a novel method for classifying adversaries that is designed to achieve social complexity reduction in large-scale, open multiagent systems. In contrast to previous work on opponent modelling, we seek to generalise from individuals and to identify suitable opponent classes. To validate the adequacy of our approach, we present initial experiments in a multiagent Iterated Prisoner’s Dilemma seenario and we discuss directions for future work on the subject.

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