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Multiagent Learning Paradigms.
Karl Tuyls and Peter Stone.
In
Francesco Belardinelli and Estefania Argente, editors, Multi-Agent Systems and Agreement Technologies, Lecture Notes
in Artificial Intelligence, pp. 3–21, Springer, 2018.
This articles examines multiagent learning from several paradigmatic perspectives, aiming to bring them together within one framework. We aim to provide a general definition of multiagent learning and lay out the essential characteristics of the various paradigms in a systematic manner by dissecting multiagent learning into its main components. We show how these various paradigms are related and describe similar learning processes but from varying perspectives, e.g. an individual (cognitive) learner vs. a population of (simple) learning agents.
@incollection{LNAI18-Tuyls,
author={Karl Tuyls and Peter Stone},
title = {Multiagent Learning Paradigms},
booktitle = {Multi-Agent Systems and Agreement Technologies},
Editor = {Francesco Belardinelli and Estefania Argente},
Publisher={Springer},
volume = {10767},
year={2018},
pages={3--21},
series={Lecture Notes in Artificial Intelligence},
abstract={
This articles examines multiagent learning from several
paradigmatic perspectives, aiming to bring them together within
one framework. We aim to provide a general definition of
multiagent learning and lay out the essential characteristics of
the various paradigms in a systematic manner by dissecting
multiagent learning into its main components. We show how these
various paradigms are related and describe similar learning
processes but from varying perspectives, e.g. an individual
(cognitive) learner vs. a population of (simple) learning
agents.
}
}
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