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Machine Learning for Agents and Multi-Agent Systems

Machine Learning for Agents and Multi-Agent Systems

Daniel Kudenko, Dimitar Kazakov, Eduardo Alonso
Copyright: © 2008 |Pages: 18
ISBN13: 9781599049410|ISBN10: 1599049414|EISBN13: 9781599049427
DOI: 10.4018/978-1-59904-941-0.ch023
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MLA

Kudenko, Daniel, et al. "Machine Learning for Agents and Multi-Agent Systems." Intelligent Information Technologies: Concepts, Methodologies, Tools, and Applications, edited by Vijayan Sugumaran, IGI Global, 2008, pp. 403-420. https://doi.org/10.4018/978-1-59904-941-0.ch023

APA

Kudenko, D., Kazakov, D., & Alonso, E. (2008). Machine Learning for Agents and Multi-Agent Systems. In V. Sugumaran (Ed.), Intelligent Information Technologies: Concepts, Methodologies, Tools, and Applications (pp. 403-420). IGI Global. https://doi.org/10.4018/978-1-59904-941-0.ch023

Chicago

Kudenko, Daniel, Dimitar Kazakov, and Eduardo Alonso. "Machine Learning for Agents and Multi-Agent Systems." In Intelligent Information Technologies: Concepts, Methodologies, Tools, and Applications, edited by Vijayan Sugumaran, 403-420. Hershey, PA: IGI Global, 2008. https://doi.org/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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