Efficient Methods for Constraint Acquisition

Abstract

Constraint acquisition systems such as QuAcq and MultiAcq can assist non-expert users to model their problems as constraint networks by classifying (partial) examples as positive or negative. For each negative example, the former focuses on one constraint of the target network, while the latter can learn a maximum number of constraints. Two bottlenecks of the acquisition process where both these algorithms encounter problems are the large number of queries required to reach convergence, and the high cpu times needed to generate queries, especially near convergence. We propose methods that deal with both these issues. The first one is an algorithm that blends the main idea of MultiAcq into QuAcq resulting in a method that learns as many constraints as MultiAcq does after a negative example, but with a lower complexity. The second is a technique that helps reduce the number of queries significantly. The third is based on the use of partial queries to cut down the time required for convergence. Experiments demonstrate that our resulting algorithm, which integrates all the new techniques, does not only generate considerably fewer queries than QuAcq and MultiAcq, but it is also by far faster than both of them, both in average query generation time and in total run time.