### Abstract

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.

Original language | English |
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Title of host publication | Principles and Practice of Multi-Agent Systems - 13th International Conference, PRIMA 2010, Revised Selected Papers |

Pages | 166-180 |

Number of pages | 15 |

DOIs | |

Publication status | Published - Dec 1 2012 |

Event | 13th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2010 - Kolkata, India Duration: Nov 12 2010 → Nov 15 2010 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 7057 LNAI |

ISSN (Print) | 0302-9743 |

ISSN (Electronic) | 1611-3349 |

### Other

Other | 13th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2010 |
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Country | India |

City | Kolkata |

Period | 11/12/10 → 11/15/10 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Theoretical Computer Science
- Computer Science(all)

### Cite this

*Principles and Practice of Multi-Agent Systems - 13th International Conference, PRIMA 2010, Revised Selected Papers*(pp. 166-180). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7057 LNAI). https://doi.org/10.1007/978-3-642-25920-3_12

**Effect of DisCSP variable-ordering heuristics in scale-free networks.** / Okimoto, Tenda; Iwasaki, Atsushi; Yokoo, Makoto.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Principles and Practice of Multi-Agent Systems - 13th International Conference, PRIMA 2010, Revised Selected Papers.*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7057 LNAI, pp. 166-180, 13th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2010, Kolkata, India, 11/12/10. https://doi.org/10.1007/978-3-642-25920-3_12

}

TY - GEN

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

AU - Okimoto, Tenda

AU - Iwasaki, Atsushi

AU - Yokoo, Makoto

PY - 2012/12/1

Y1 - 2012/12/1

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

ER -