Selecting the Appropriate Consistency Algorithm for CSPs Using Machine Learning Classifiers

Authors

  • Daniel Geschwender University of Nebraska - Lincoln
  • Shant Karakashian University of Nebraska - Lincoln
  • Robert Woodward University of Nebraska - Lincoln
  • Berthe Choueiry University of Nebraska - Lincoln
  • Stephen Scott University of Nebraska - Lincoln

DOI:

https://doi.org/10.1609/aaai.v27i1.8532

Keywords:

Constraint Satisfaction, Machine Learning

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.

AAAI 2013 Proceedings Cover

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Published

2013-06-29

How to Cite

Geschwender, D., Karakashian, S., Woodward, R., Choueiry, B., & Scott, S. (2013). Selecting the Appropriate Consistency Algorithm for CSPs Using Machine Learning Classifiers. Proceedings of the AAAI Conference on Artificial Intelligence, 27(1), 1611-1612. https://doi.org/10.1609/aaai.v27i1.8532

Issue

Vol. 27 No. 1 (2013): Twenty-Seventh AAAI Conference on Artificial Intelligence

Section

Pre-PhD Student Abstracts