Effect of nogood learning in distributed constraint satisfaction

Katsutoshi Hirayama, Makoto Yokoo

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

37 被引用数 (Scopus)

抄録

We present resolvent-based learning as a new nogood learning method for a distributed constraint satisfaction algorithm. This method is based on a look-back technique in constraint satisfaction algorithms and can efficiently make effective nogoods. We combine the method with the asynchronous weak-commitment search algorithm (AWC) and evaluate the performance of the resultant algorithm on distributed 3-coloring problems and distributed 3SAT problems. As a result, we found that the resolvent-based learning works well compared to previous learning methods for distributed constraint satisfaction algorithms. We also found that the AWC with the resolvent-based learning is able to find a solution with fewer cycles than the distributed breakout algorithm, which was known to be the most efficient algorithm (in terms of cycles) for solving distributed constraint satisfaction problems.

本文言語英語
ホスト出版物のタイトルProceedings - International Conference on Distributed Computing Systems
出版社IEEE
ページ169-177
ページ数9
出版ステータス出版済み - 2000
外部発表はい
イベント20th International Conference on Distributed Computing Systems (ICDCS 2000) - Taipei, Taiwan
継続期間: 4 10 20004 13 2000

その他

その他20th International Conference on Distributed Computing Systems (ICDCS 2000)
CityTaipei, Taiwan
Period4/10/004/13/00

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture

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