TY - JOUR
T1 - Multi-agent oriented constraint satisfaction
AU - Liu, Jiming
AU - Jing, Han
AU - Tang, Y. Y.
N1 - Funding Information:
We would like to thank the reviewers and associate editor for their valuable comments and suggestions, which have led us to examine several related issues and at the same time make the paper easier to follow. We also want to thank Xiadong Jin for proof-reading our paper. Our work has been partially supported by an HKBU FRG research grant.
PY - 2002/3
Y1 - 2002/3
N2 - This paper presents a multi-agent oriented method for solving CSPs (Constraint Satisfaction Problems). In this method, distributed agents represent variables and a two-dimensional grid-like environment in which the agents inhabit corresponds to the domains of the variables. Thus, the positions of the agents in such an environment constitute the solution to a CSP. In order to reach a solution state, the agents will rely on predefined local reactive behaviors; namely, better-move, least-move, and random-move. While presenting the formalisms and algorithm, we will analyze the correctness and complexity of the algorithm, and demonstrate the proposed method with two benchmark CSPs, i.e., n-queen problems and coloring problems. In order to further determine the effectiveness of different reactive behaviors, we will examine the performance of this method in deriving solutions based on behavior prioritization and different selection probabiities.
AB - This paper presents a multi-agent oriented method for solving CSPs (Constraint Satisfaction Problems). In this method, distributed agents represent variables and a two-dimensional grid-like environment in which the agents inhabit corresponds to the domains of the variables. Thus, the positions of the agents in such an environment constitute the solution to a CSP. In order to reach a solution state, the agents will rely on predefined local reactive behaviors; namely, better-move, least-move, and random-move. While presenting the formalisms and algorithm, we will analyze the correctness and complexity of the algorithm, and demonstrate the proposed method with two benchmark CSPs, i.e., n-queen problems and coloring problems. In order to further determine the effectiveness of different reactive behaviors, we will examine the performance of this method in deriving solutions based on behavior prioritization and different selection probabiities.
KW - Behavior prioritization
KW - Behavior selection
KW - Constraint satisfaction
KW - Experimental validation
KW - Multi-agent
KW - Reactive moving behaviors
UR - http://www.scopus.com/inward/record.url?scp=0036498333&partnerID=8YFLogxK
U2 - 10.1016/S0004-3702(01)00174-6
DO - 10.1016/S0004-3702(01)00174-6
M3 - Journal article
AN - SCOPUS:0036498333
SN - 0004-3702
VL - 136
SP - 101
EP - 144
JO - Artificial Intelligence
JF - Artificial Intelligence
IS - 1
ER -