Selecting the Appropriate Consistency Algorithm for CSPs Using Machine Learning Classifiers

Daniel J. Geschwender, Shant Karakashian, Robert J. Woodward, Berthe Y. Choueiry, Stephen D. Scott

AAAI 2013 pp. 1611-1612

doi:10.1609/AAAI.V27I1.8532 /aaai/2013/geschwender2013aaai-selecting/

Abstract

Computing the minimal network of a Constraint Satisfaction Problem (CSP) is a useful and difficult task. Two algorithms, PerTuple and AllSol, were proposed to this end. The performances of these algorithms vary with the problem instance. We use Machine Learning techniques to build a classifier that predicts which of the two algorithms is likely to be more effective.

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Cite

Text

Geschwender et al. "Selecting the Appropriate Consistency Algorithm for CSPs Using Machine Learning Classifiers." AAAI Conference on Artificial Intelligence, 2013. doi:10.1609/AAAI.V27I1.8532

Markdown

[Geschwender et al. "Selecting the Appropriate Consistency Algorithm for CSPs Using Machine Learning Classifiers." AAAI Conference on Artificial Intelligence, 2013.](https://mlanthology.org/aaai/2013/geschwender2013aaai-selecting/) doi:10.1609/AAAI.V27I1.8532

BibTeX

@inproceedings{geschwender2013aaai-selecting,
  title     = {{Selecting the Appropriate Consistency Algorithm for CSPs Using Machine Learning Classifiers}},
  author    = {Geschwender, Daniel J. and Karakashian, Shant and Woodward, Robert J. and Choueiry, Berthe Y. and Scott, Stephen D.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2013},
  pages     = {1611-1612},
  doi       = {10.1609/AAAI.V27I1.8532},
  url       = {https://mlanthology.org/aaai/2013/geschwender2013aaai-selecting/}
}