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Multiagent Systems: A survey from a machine learning perspective.
Peter
Stone and Manuela Veloso.
Autonomous Robots, 8(3):345–383, July
2000.
Formerly citable as Carnegie Mellon University CS technical report number CMU-CS-97-193. December,
1997.
[PDF]292.1kB [postscript]1.1MB
Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is concerned with systems that consist of multiple independent entities that interact in a domain. Traditionally, DAI has been divided into two sub-disciplines: Distributed Problem Solving (DPS) focuses on the information management aspects of systems with several components working together towards a common goal; Multiagent Systems (MAS) deals with behavior management in collections of several independent entities, or agents. This survey of MAS is intended to serve as an introduction to the field and as an organizational framework. A series of general multiagent scenarios are presented. For each scenario, the issues that arise are described along with a sampling of the techniques that exist to deal with them. The presented techniques are not exhaustive, but they highlight how multiagent systems can be and have been used to build complex systems. When options exist, the techniques presented are biased towards machine learning approaches. Additional opportunities for applying machine learning to MAS are highlighted and robotic soccer is presented as an appropriate test bed for MAS. This survey does not focus exclusively on robotic systems. However, we believe that much of the prior research in non-robotic MAS is relevant to robotic MAS, and we explicitly discuss several robotic MAS, including all of those presented in this issue.
@Article(MASsurvey,
Author="Peter Stone and Manuela Veloso",
Title="Multiagent Systems: {A} survey from a machine learning perspective",
Journal="Autonomous Robots",
Volume="8",
Number="3",
Year="2000",
Month="July",
pages="345--383",
abstract={
Distributed Artificial Intelligence (DAI) has existed
as a subfield of AI for less than two decades. DAI is
concerned with systems that consist of multiple
independent entities that interact in a domain.
Traditionally, DAI has been divided into two
sub-disciplines: Distributed Problem Solving (DPS)
focuses on the information management aspects of
systems with several components working together
towards a common goal; Multiagent Systems (MAS) deals
with behavior management in collections of several
independent entities, or agents. This survey of MAS is
intended to serve as an introduction to the field and
as an organizational framework. A series of general
multiagent scenarios are presented. For each scenario,
the issues that arise are described along with a
sampling of the techniques that exist to deal with
them. The presented techniques are not exhaustive, but
they highlight how multiagent systems can be and have
been used to build complex systems. When options
exist, the techniques presented are biased towards
machine learning approaches. Additional opportunities
for applying machine learning to MAS are highlighted
and robotic soccer is presented as an appropriate test
bed for MAS. This survey does not focus exclusively on
robotic systems. However, we believe that much of the
prior research in non-robotic MAS is relevant to
robotic MAS, and we explicitly discuss several robotic
MAS, including all of those presented in this issue.
},
wwwnote={Formerly citable as Carnegie Mellon University CS
technical report number CMU-CS-97-193. December, 1997.},
)
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