6 Learning in Multiagent Systems Sandip Sen and Gerhard Weiss 6.1 Introduction Learning and intelligence are intimately related to each other. It is usually agreed that a system capable of learning deserves to be called intelligent; and conversely, a system being considered as intelligent is, among other things, usually expected to be able to learn. Learning always has to do with the self-improvement of future behavior based on past experience. More precisely, according to the standard artificial intelligence (AI) point of view learning can be informally defined as follows: The acquisition of new knowledge and motor and cognitive skills and the incorporation of the acquired knowledge and skills in future system activities, provided that this acquisition and incorporation is conducted by the system itself and leads to an improvement in its performance. This definition also serves as a basis for this chapter. Machine learning (ML), as one of the core fields of AI, is concerned with the computational aspects of learning in natural as well as technical systems. It is beyond the scope and intention of this chapter to offer an introduction to the broad and well developed field of ML. Instead, it introduces the reader into learning in multiagent systems and, with that, into a subfield of both ML and distributed AI (DAI). The chapter is written such that it can be understood without requiring familiarity with ML. The intersection of DAI and ML constitutes a young but important area of research and application. The DAI and the ML communities largely ignored this area for a long time (there are exceptions on both sides, but they just prove the rule). On the one hand, work in DAI was mainly concerned with multiagent systems whose structural organization and functional behavior typically were determined in detail and therefore were more or less fixed. On the other hand, work in ML primarily dealt with learning as a centralized and isolated process that occurs in intelligent stand-alone systems. In the past this mutual ignorance of DAI and ML has disappeared, and today the area of learning in multiagent systems receives broad and steadily increasing attention. This is also reflected by the growing number of publications in this area; see [23, 24, 43, 45, 64, 66, 68] for collections of papers related to learning in multiagent systems. There are two major reasons for this attention, both showing the importance of bringing DAI and ML together: