### Abstract

The min-conflict heuristic (Minton et al. 1992) has been introduced into backtracking algorithms and iterative improvement algorithms as a powerful heuristic for solving constraint satisfaction problems. Backtracking algorithms become inefficient when a bad partial solution is constructed, since an exhaustive search is required for revising the bad decision. On the other hand, iterative improvement algorithms do not construct a consistent partial solution and can revise a bad decision without exhaustive search. However, most of the powerful heuristics obtained through the long history of constraint satisfaction studies (e.g., forward checking (Haralick & Elliot 1980)) presuppose the existence of a consistent partial solution. Therefore, these heuristics can not be applied to iterative improvement algorithms. Furthermore, these algorithms are not theoretically complete. In this paper, a new algorithm called weak-commitment search which utilizes the min-conflict heuristic is developed. This algorithm removes the drawbacks of backtracking algorithms and iterative improvement algorithms, i.e., the algorithm can revise bad decisions without exhaustive search, the completeness of the algorithm is guaranteed, and various heuristics can be introduced since a consistent partial solution is constructed. The experimental results on various example problems show that this algorithm is 3 to 10 times more efficient than other algorithms.

Original language | English |
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Title of host publication | Proceedings of the National Conference on Artificial Intelligence |

Publisher | AAAI |

Pages | 313-318 |

Number of pages | 6 |

Volume | 1 |

Publication status | Published - 1994 |

Externally published | Yes |

Event | Proceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2) - Seattle, WA, USA Duration: Jul 31 1994 → Aug 4 1994 |

### Other

Other | Proceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2) |
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City | Seattle, WA, USA |

Period | 7/31/94 → 8/4/94 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Software

### Cite this

*Proceedings of the National Conference on Artificial Intelligence*(Vol. 1, pp. 313-318). AAAI.

**Weak-commitment search for solving constraint satisfaction problems.** / Yokoo, Makoto.

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

*Proceedings of the National Conference on Artificial Intelligence.*vol. 1, AAAI, pp. 313-318, Proceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2), Seattle, WA, USA, 7/31/94.

}

TY - GEN

T1 - Weak-commitment search for solving constraint satisfaction problems

AU - Yokoo, Makoto

PY - 1994

Y1 - 1994

N2 - The min-conflict heuristic (Minton et al. 1992) has been introduced into backtracking algorithms and iterative improvement algorithms as a powerful heuristic for solving constraint satisfaction problems. Backtracking algorithms become inefficient when a bad partial solution is constructed, since an exhaustive search is required for revising the bad decision. On the other hand, iterative improvement algorithms do not construct a consistent partial solution and can revise a bad decision without exhaustive search. However, most of the powerful heuristics obtained through the long history of constraint satisfaction studies (e.g., forward checking (Haralick & Elliot 1980)) presuppose the existence of a consistent partial solution. Therefore, these heuristics can not be applied to iterative improvement algorithms. Furthermore, these algorithms are not theoretically complete. In this paper, a new algorithm called weak-commitment search which utilizes the min-conflict heuristic is developed. This algorithm removes the drawbacks of backtracking algorithms and iterative improvement algorithms, i.e., the algorithm can revise bad decisions without exhaustive search, the completeness of the algorithm is guaranteed, and various heuristics can be introduced since a consistent partial solution is constructed. The experimental results on various example problems show that this algorithm is 3 to 10 times more efficient than other algorithms.

AB - The min-conflict heuristic (Minton et al. 1992) has been introduced into backtracking algorithms and iterative improvement algorithms as a powerful heuristic for solving constraint satisfaction problems. Backtracking algorithms become inefficient when a bad partial solution is constructed, since an exhaustive search is required for revising the bad decision. On the other hand, iterative improvement algorithms do not construct a consistent partial solution and can revise a bad decision without exhaustive search. However, most of the powerful heuristics obtained through the long history of constraint satisfaction studies (e.g., forward checking (Haralick & Elliot 1980)) presuppose the existence of a consistent partial solution. Therefore, these heuristics can not be applied to iterative improvement algorithms. Furthermore, these algorithms are not theoretically complete. In this paper, a new algorithm called weak-commitment search which utilizes the min-conflict heuristic is developed. This algorithm removes the drawbacks of backtracking algorithms and iterative improvement algorithms, i.e., the algorithm can revise bad decisions without exhaustive search, the completeness of the algorithm is guaranteed, and various heuristics can be introduced since a consistent partial solution is constructed. The experimental results on various example problems show that this algorithm is 3 to 10 times more efficient than other algorithms.

UR - http://www.scopus.com/inward/record.url?scp=0028588435&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0028588435&partnerID=8YFLogxK

M3 - Conference contribution

VL - 1

SP - 313

EP - 318

BT - Proceedings of the National Conference on Artificial Intelligence

PB - AAAI

ER -