Vidal's library
Title: Applying Constraint Reasoning to Real-world Distributed Task Allocation
Author: Paul Scerri, Pragnesh Jay Modi, WeiMin Shen, and Milind Tambe
Book Tittle: Proceedings of Autonomous Agents and Multi-Agent Systems Workshop on Distributed Constraint Reasoning
Pages: 134--141
Year: 2002
Abstract: Distributed task allocation algorithms requires a set of agents to intelligently allocate their resources to a set of tasks. The problem is often complicated by the fact that resources may be limited, the set of tasks may not be exactly known, and the set of tasks may change over time. Previous resource allocation algorithms have not been able to handle over- constrained situations, the uncertainty in the environment and/or dynamics. In this paper, we present extensions to an algorithm for distributed constraint optimization, called Adopt-SC which allows it to be applied in such real-world domains. The approach relies on maintaining a probabil- ity distribution over tasks that are potentially present. The distribution is updated with both information from local sensors and information inferred from communication be- tween agents. We present promising results with the ap- proach on a distributed task allocation problem consisting of a set of stationary sensors that must track a moving tar- get. The techniques proposed in this paper are evaluated on real hardware tracking real moving targets.

Cited by 2  -  Google Scholar

@InProceedings{scerri02a,
  author =	 {Paul Scerri and Pragnesh Jay Modi and WeiMin Shen
                  and Milind Tambe},
  title =	 {Applying Constraint Reasoning to Real-world
                  Distributed Task Allocation},
  booktitle =	 {Proceedings of Autonomous Agents and Multi-Agent
                  Systems Workshop on Distributed Constraint
                  Reasoning},
  pages =	 {134--141},
  year =	 2002,
  comment =	 {The Adopt-SC algorithm.},
  abstract =	 {Distributed task allocation algorithms requires a
                  set of agents to intelligently allocate their
                  resources to a set of tasks. The problem is often
                  complicated by the fact that resources may be
                  limited, the set of tasks may not be exactly known,
                  and the set of tasks may change over time. Previous
                  resource allocation algorithms have not been able to
                  handle over- constrained situations, the uncertainty
                  in the environment and/or dynamics. In this paper,
                  we present extensions to an algorithm for
                  distributed constraint optimization, called Adopt-SC
                  which allows it to be applied in such real-world
                  domains. The approach relies on maintaining a
                  probabil- ity distribution over tasks that are
                  potentially present. The distribution is updated
                  with both information from local sensors and
                  information inferred from communication be- tween
                  agents. We present promising results with the ap-
                  proach on a distributed task allocation problem
                  consisting of a set of stationary sensors that must
                  track a moving tar- get. The techniques proposed in
                  this paper are evaluated on real hardware tracking
                  real moving targets.},
  keywords =     {multiagent dcsp},
  url =		 {http://jmvidal.cse.sc.edu/library/scerri02a.pdf},
  googleid = 	 {qsvdjGF3Mk0J:scholar.google.com/},
  cluster = 	 {5562639750614862762}
}
Last modified: Wed Mar 9 10:15:30 EST 2011