TY - GEN

T1 - Effect of DisCSP variable-ordering heuristics in scale-free networks

AU - Okimoto, Tenda

AU - Iwasaki, Atsushi

AU - Yokoo, Makoto

PY - 2012

Y1 - 2012

N2 - A Distributed Constraint Satisfaction Problem (DisCSP) is a constraint satisfaction problem in which variables and constraints are distributed among multiple agents. Various algorithms for solving DisC-SPs have been developed, which are intended for general purposes, i.e., they can be applied to any network structure. However, if a network has some particular structure, e.g., the network structure is scale-free, we can expect that some specialized algorithms or heuristics, which are tuned for the network structure, can outperform general purpose algorithms/heuristics. In this paper, as an initial step toward developing specialized algorithms for particular network structures, we examine variable-ordering heuristics in scale-free networks. We use the classic asynchronous backtracking algorithm as a baseline algorithm and examine the effect of variable-ordering heuristics. First, we show that the choice of variable-ordering heuristics is more influential in scale-free networks than in random networks. Furthermore, we develop a novel variable-ordering heuristic that is specialized to scale-free networks. Experimental results illustrate that our new variable-ordering heuristic is more effective than a standard degree-based variable-ordering heuristic. Our proposed heuristic reduces the required cycles by 30% at the critical point.

AB - A Distributed Constraint Satisfaction Problem (DisCSP) is a constraint satisfaction problem in which variables and constraints are distributed among multiple agents. Various algorithms for solving DisC-SPs have been developed, which are intended for general purposes, i.e., they can be applied to any network structure. However, if a network has some particular structure, e.g., the network structure is scale-free, we can expect that some specialized algorithms or heuristics, which are tuned for the network structure, can outperform general purpose algorithms/heuristics. In this paper, as an initial step toward developing specialized algorithms for particular network structures, we examine variable-ordering heuristics in scale-free networks. We use the classic asynchronous backtracking algorithm as a baseline algorithm and examine the effect of variable-ordering heuristics. First, we show that the choice of variable-ordering heuristics is more influential in scale-free networks than in random networks. Furthermore, we develop a novel variable-ordering heuristic that is specialized to scale-free networks. Experimental results illustrate that our new variable-ordering heuristic is more effective than a standard degree-based variable-ordering heuristic. Our proposed heuristic reduces the required cycles by 30% at the critical point.

UR - http://www.scopus.com/inward/record.url?scp=84887254099&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84887254099&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-25920-3_12

DO - 10.1007/978-3-642-25920-3_12

M3 - Conference contribution

AN - SCOPUS:84887254099

SN - 9783642259197

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 166

EP - 180

BT - Principles and Practice of Multi-Agent Systems - 13th International Conference, PRIMA 2010, Revised Selected Papers

T2 - 13th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2010

Y2 - 12 November 2010 through 15 November 2010

ER -