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KI - Künstliche Intelligenz
Erschienen in: KI - Künstliche Intelligenz 3/2014

01.08.2014 | Technical Contribution

Beyond Reinforcement Learning and Local View in Multiagent Systems

verfasst von: Ana L. C. Bazzan

Erschienen in: KI - Künstliche Intelligenz | Ausgabe 3/2014

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Abstract

Learning is an important component of an agent’s decision making process. Despite many messages in contrary, the fact is that, currently, in the multiagent community it is mostly likely that learning means reinforcement learning. Given this background, this paper has two aims: to revisit the “old days” motivations for multiagent learning, and to describe some of the work addressing the frontiers of multiagent systems and machine learning. The intention of the latter task is to try to motivate people to address the issues that are involved in the application of techniques from multiagent systems in machine learning and vice-versa.
Vorheriger Artikel Measuring Inconsistency in Multi-Agent Systems
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Fußnoten
1
Henceforth, with some abuse, I use the term ML to designate supervised and unsupervised ML techniques. I do so because this is the view taken by most of the computer science community, not to mention other communities.
 
2
It should be noted that back in 2000, game theoretic approaches were not necessarily combined with RL.
 
3
In this paper, details about RL and MARL, as well as Markov decision processes, stochastic games, and Q-learning are omitted. The reader is referred to[10, 27, 40, 60].
 
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Metadaten
Titel
Beyond Reinforcement Learning and Local View in Multiagent Systems
verfasst von
Ana L. C. Bazzan
Publikationsdatum
01.08.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
KI - Künstliche Intelligenz / Ausgabe 3/2014
Print ISSN: 0933-1875
Elektronische ISSN: 1610-1987
DOI
https://doi.org/10.1007/s13218-014-0312-5

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