Machine Learning for Agents and Multi-Agent Systems

Machine Learning for Agents and Multi-Agent Systems

Daniel Kudenko (University of York, UK), Dimitar Kazakov (University of York, UK) and Eduardo Alonso (City University, UK)
DOI: 10.4018/978-1-59904-941-0.ch023
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Abstract

In order to be truly autonomous, agents need the ability to learn from and adapt to the environment and other agents. This chapter introduces key concepts of machine learning and how they apply to agent and multi-agent systems. Rather than present a comprehensive survey, we discuss a number of issues that we believe are important in the design of learning agents and multi-agent systems. Specifically, we focus on the challenges involved in adapting (originally disembodied) machine learning techniques to situated agents, the relationship between learning and communication, learning to collaborate and compete, learning of roles, evolution and natural selection, and distributed learning. In the second part of the chapter, we focus on some practicalities and present two case studies.

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