Paper Info
Title
An agent model for binary constraint satisfaction problems
Abstract
With the intrinsic properties of constraint satisfaction problems (CSPs) in mind, several behaviors are designed for agents by making use of the ability of agents to sense and act on the environment. These behaviors are controlled by means of evolution, so that multiagent evolutionary algorithm for constraint satisfaction problems (MAEA-CSPs) results. To overcome the disadvantages of the general encoding methods, the minimum conflict encoding is also proposed. The experiments use 250 benchmark CSPs to test the performance of MAEA-CSPs, and compare it with four well-defined algorithms. The results show that MAEA-CSPs outperforms the other methods. In addition, the effect of the parameters is analyzed systematically.
Year
DOI
Venue
2005
10.1007/978-3-540-31996-2_24
EvoCOP
Keywords
Field
DocType
multiagent evolutionary algorithm,well-defined algorithm,general encoding method,binary constraint satisfaction problem,agent model,minimum conflict encoding,constraint satisfaction problem,intrinsic property,benchmark csps,evolutionary algorithm
Constraint satisfaction,Mathematical optimization,Local consistency,Evolutionary algorithm,Computer science,Constraint satisfaction problem,Constraint satisfaction dual problem,Backtracking,Binary constraint,Hybrid algorithm (constraint satisfaction)
Conference
Volume
ISSN
ISBN
3448
0302-9743
3-540-25337-8
Citations 
PageRank 
References 
0
0.34
5
Authors
3
Name
Order
Citations
PageRank
Weicai Zhong138126.14
Jing Liu21043115.54
Licheng Jiao35698475.84