Multiagent Learning: Basics, Challenges, and Prospects

Karl Tuyls; Gerhard Weiss

doi:10.1609/aimag.v33i3.2426

Multiagent Learning: Basics, Challenges, and Prospects

Karl Tuyls^*, Gerhard Weiss

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Multiagent systems (MAS) are widely accepted as an important method for solving problems of a distributed nature. A key to the success of MAS is efficient and effective multiagent learning (MAL). The past 25 years have seen a great interest and tremendous progress in the field of MAL. This article introduces and overviews this field by presenting its fundamentals, sketching its historical development, and describing some key algorithms for MAL. Moreover, main challenges that the field is facing today are identified.

Original language	English
Pages (from-to)	41-52
Number of pages	13
Journal	Ai Magazine
Volume	33
Issue number	3
DOIs	https://doi.org/10.1609/aimag.v33i3.2426
Publication status	Published - 1 Oct 2012

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10.1609/aimag.v33i3.2426

Cite this

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abstract = "Multiagent systems (MAS) are widely accepted as an important method for solving problems of a distributed nature. A key to the success of MAS is efficient and effective multiagent learning (MAL). The past 25 years have seen a great interest and tremendous progress in the field of MAL. This article introduces and overviews this field by presenting its fundamentals, sketching its historical development, and describing some key algorithms for MAL. Moreover, main challenges that the field is facing today are identified.",

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