  We introduce the topic of learning in multiagent systems. We first provide a
quick introduction to the field of game theory, focusing on the equilibrium
concepts of iterated dominance, and Nash equilibrium. We show some of the most
relevant findings in the theory of learning in games, including theorems on
fictitious play, replicator dynamics, and evolutionary stable strategies. The
CLRI theory and n-level learning agents are introduced as attempts to apply
some of these findings to the problem of engineering multiagent systems with
learning agents. Finally, we summarize some of the remaining challenges in the
field of learning in multiagent systems.
