Vidal's library
Title: 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