Parallel Constraint Acquisition

Authors

  • Nadjib Lazaar LIRMM University of Montpellier CNRS

Keywords:

Constraint Learning and Acquisition

Abstract

Constraint acquisition systems assist the non-expert user in modelling her problem as a constraint network. QUACQ is a sequential constraint acquisition algorithm that generates queries as (partial) examples to be classified as positive or negative. The drawbacks are that the user may need to answer a great number of such examples, within a significant waiting time between two examples, to learn all the constraints. In this paper, we propose PACQ, a portfolio-based parallel constraint acquisition system. The design of PACQ benefits from having several users sharing the same target problem. Moreover, each user is involved in a particular acquisition session, opened in parallel to improve the overall performance of the whole system.We prove the correctness of PACQ and we give an experimental evaluation that shows that our approach improves on QUACQ.

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Published

2021-05-18

How to Cite

Lazaar, N. (2021). Parallel Constraint Acquisition. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5), 3860-3867. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16504

Issue

Section

AAAI Technical Track on Constraint Satisfaction and Optimization