Vidal's libraryTitle: | 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