You are here

Robust Constraint Acquisition by Sequential Analysis

Authors: 
Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
Modeling a combinatorial problem is a hard and error-prone task requiring expertise. Constraint acquisition methods can automate this process by learning constraints from examples of solutions and (usually) non-solutions. We describe a new statistical approach based on sequential analysis that is orders of magnitude faster than existing methods, and gives accurate results on popular benchmarks. It is also robust in the sense that it can learn constraints correctly even when the data contain many errors.
Conference Name: 
ECAI 2020
Proceedings: 
24th European Conference on Artificial Intelligence
Digital Object Identifer (DOI): 
10.3233/FAIA200113
Publication Date: 
29/08/2020
Volume: 
325
Pages: 
355-362
Conference Location: 
Spain
Institution: 
National University of Ireland, Cork (UCC)
Open access repository: 
Yes