Recommended Reading
Requisite background in game theory can be obtained from the many introductory texts, and most compactly from Leyton-Brown (2008). Game theoretic work on multi-agent learning is covered in Fudenberg (1998) and Young (2004). An expanded discussion of the problems addressed under the header of MAL can be found in Shoham et al. (2007), and the responses to it in Vohra (2007). Discussion of MAL algorithms, both traditional and more novel ones, can be found in the above references, as well as in Greenwald (2007).
Fudenberg, D., & Levine, D. (1998). The theory of learning in games. Cambridge: MIT Press.
Greenwald, A., & Littman, M. L. (Eds.). (2007). Special issue on learning and computational game theory. Machine Learning 67(1–2).
Leyton-Brown, K., & Shoham, Y. (2008). Essentials of game theory. San Rafael, CA: Morgan and Claypool.
Shoham, Y., Powers, W. R., & Grenager, T. (2007). If multiagent learning is the answer, what is the question? Artificial Intelligence, 171(1), 365–377. Special issue on foundations of multiagent learning.
Vohra, R., & Wellman, M. P. (Eds.). (2007). Special issue on foundations of multiagent learning. Artificial Intelligence, 171(1).
Young, H. P. (2004). Strategic learning and its limits. Oxford: Oxford University Press.
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Shoham, Y., Powers, R. (2011). Multi-Agent Learning I: Problem Definition. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_563
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