Peter Stone's Selected Publications

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Multiagent Systems: A survey from a machine learning perspective

Peter Stone and Manuela Veloso. Multiagent Systems: A survey from a machine learning perspective. Autonomous Robots, 8(3):345–383, July 2000.
Formerly citable as Carnegie Mellon University CS technical report number CMU-CS-97-193. December, 1997.

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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.

BibTeX Entry

@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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