Multiagent learning is not the answer. It is the question

Authors:
Peter Stone
Affiliations:
Department of Computer Sciences, The University of Texas at Austin, 1 University Station C0500, Austin, TX 78712-1188, USA
Venue:
Artificial Intelligence
Year:
2007

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Abstract

The article by Shoham, Powers, and Grenager called ''If multi-agent learning is the answer, what is the question?'' does a great job of laying out the current state of the art and open issues at the intersection of game theory and artificial intelligence (AI). However, from the AI perspective, the term ''multiagent learning'' applies more broadly than can be usefully framed in game theoretic terms. In this larger context, how (and perhaps whether) multiagent learning can be usefully applied in complex domains is still a large open question.