KI - Künstliche Intelligenz

, Volume 28, Issue 3, pp 179–189

Beyond Reinforcement Learning and Local View in Multiagent Systems

Technical Contribution

DOI: 10.1007/s13218-014-0312-5

Cite this article as:
Bazzan, A.L.C. Künstl Intell (2014) 28: 179. doi:10.1007/s13218-014-0312-5

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.

Keywords

Multiagent systems Multiagent learning Machine learning Distributed machine learning Reinforcement learning 

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Instituto de Informática, UFRGSPorto AlegreBrazil

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