You are here

Classifier-based constraint acquisition

Publication Type: 
Refereed Original Article
Modeling a combinatorial problem is a hard and error-prone task requiring significant expertise. Constraint acquisition methods attempt to automate this process by learning constraints from examples of solutions and (usually) non-solutions. Active methods query an oracle while passive methods do not. We propose a known but not widely-used application of machine learning to constraint acquisition: training a classifier to discriminate between solutions and non-solutions, then deriving a constraint model from the trained classifier. We discuss a wide range of possible new acquisition methods with useful properties inherited from classifiers. We also show the potential of this approach using a Naive Bayes classifier, obtaining a new passive acquisition algorithm that is considerably faster than existing methods, scalable to large constraint sets, and robust under errors.
Digital Object Identifer (DOI): 
Publication Status: 
Date Accepted for Publication: 
Monday, 1 March, 2021
Publication Date: 
Annals of Mathematics and Artificial Intelligence
National University of Ireland, Cork (UCC)
Open access repository: