If multi-agent learning is the answer, what is the question?

Abstract

The area of learning in multi-agent systems is today one of the most fertile grounds for interaction between game theory and artificial intelligence. We focus on the foundational questions in this interdisciplinary area, and identify several distinct agendas that ought to, we argue, be separated. The goal of this article is to start a discussion in the research community that will result in firmer foundations for the area. 1 1 This article has a long history and owes many debts. A first version was presented at the NIPS workshop, Multi-Agent Learning: Theory and Practice, in 2002. A later version was presented at the AAAI Fall Symposium in 2004 [Y. Shoham, R. Powers, T. Grenager, On the agenda(s) of research on multi-agent learning, in: AAAI 2004 Symposium on Artificial Multi-Agent Learning (FS-04-02), AAAI Press, 2004]. Over time it has gradually evolved into the current form, as a result of our own work in the area as well as the feedback of many colleagues. We thank them all collectively, with special thanks to members of the multi-agent group at Stanford in the past three years. Rakesh Vohra and Michael Wellman provided detailed comments on the latest draft which resulted in substantive improvements, although we alone are responsible for the views put forward. This work was supported by NSF ITR grant IIS-0205633 and DARPA grant HR0011-05-1.