Please use this identifier to cite or link to this item:
Title: Multiagent Learning: Basics, Challenges, and Prospects
Authors: TUYLS, Karl 
Weiss, Gerhard
Issue Date: 2012
Source: AI MAGAZINE, 33 (3), p. 41-52
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.
Notes: [Tuyls, Karl] Maastricht Univ, Dept Knowledge Engn, Res Grp Swarm Robot & Learning Multiagent Syst, Maastricht Swarmlab, Maastricht, Netherlands. [Tuyls, Karl] Vrije Univ Brussel, Brussels, Belgium. [Tuyls, Karl] Hasselt Univ, Hasselt, Belgium. [Tuyls, Karl] Eindhoven Univ Technol, NL-5600 MB Eindhoven, Netherlands. [Weiss, Gerhard] Software Competence Ctr Hagenberg GmbH, Hagenberg, Austria. [Weiss, Gerhard] Tech Univ Munich, Dept Comp Sci, D-8000 Munich, Germany. [Weiss, Gerhard] Maastricht Univ, Dept Knowledge Engn, Fac Humanities & Sci, Maastricht, Netherlands.
Keywords: Computer Science, Artificial Intelligence
Document URI:
ISSN: 0738-4602
e-ISSN: 0738-4602
ISI #: 000309621600004
Category: A1
Type: Journal Contribution
Validations: ecoom 2013
Appears in Collections:Research publications

Show full item record


checked on May 22, 2022

Page view(s)

checked on May 28, 2022

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.