Vidal's libraryTitle: | Distributed Breakout Algorithm for Solving Distributed Constraint Satisfaction Problems |
Author: | Makoto Yokoo and Katsutoshi Hirayama |
Book Tittle: | Proceedings of the Second International Conference on Multiagent Systems |
Pages: | 401--408 |
Year: | 1996 |
Abstract: | This paper presents a new algorithm for solving dis- tributed constraint satisfaction problems (distributed CSPs) called the distributed breakout algorithm, which is inspired by the breakout algorithm for solving centralized CSPs. In this algorithm, each agent tries to optimize its evaluation value (the number of constraint violations) by exchanging its current value and the possible amount of its improvement among neighboring agents. Instead of detecting the fact that agents as a whole are trapped in a local-minimum, each agent detects whether it is in a quasi-local-minimum, which is a weaker condition than a local-minimum, and changes the weights of constraint violations to escape from the quasi-local-minimum. Experimental evaluations show this algorithm to be much more efficient than existing algorithms for critically di cult problem instances of distributed graph-coloring problems. |
Cited by 62 - Google Scholar
@InProceedings{yokoo96a,
author = {Makoto Yokoo and Katsutoshi Hirayama},
title = {Distributed Breakout Algorithm for Solving
Distributed Constraint Satisfaction Problems},
booktitle = {Proceedings of the Second International Conference
on Multiagent Systems},
pages = {401--408},
year = 1996,
abstract = {This paper presents a new algorithm for solving dis-
tributed constraint satisfaction problems
(distributed CSPs) called the distributed breakout
algorithm, which is inspired by the breakout
algorithm for solving centralized CSPs. In this
algorithm, each agent tries to optimize its
evaluation value (the number of constraint
violations) by exchanging its current value and the
possible amount of its improvement among neighboring
agents. Instead of detecting the fact that agents as
a whole are trapped in a local-minimum, each agent
detects whether it is in a quasi-local-minimum,
which is a weaker condition than a local-minimum,
and changes the weights of constraint violations to
escape from the quasi-local-minimum. Experimental
evaluations show this algorithm to be much more
efficient than existing algorithms for critically di
cult problem instances of distributed graph-coloring
problems.},
keywords = {multiagent dcsp},
googleid = {_EnMORkzsesJ:scholar.google.com/},
url = {http://jmvidal.cse.sc.edu/library/yokoo96a.pdf},
cluster = {16983411853227739644}
}
Last modified: Wed Mar 9 10:14:05 EST 2011