Effect of nogood learning in distributed constraint satisfaction

Katsutoshi Hirayama, Makoto Yokoo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

37 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - International Conference on Distributed Computing Systems
PublisherIEEE
Pages169-177
Number of pages9
Publication statusPublished - 2000
Externally publishedYes
Event20th International Conference on Distributed Computing Systems (ICDCS 2000) - Taipei, Taiwan
Duration: Apr 10 2000Apr 13 2000

Other

Other20th 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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    Hirayama, K., & Yokoo, M. (2000). Effect of nogood learning in distributed constraint satisfaction. In Proceedings - International Conference on Distributed Computing Systems (pp. 169-177). IEEE.