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Robust Constraint Acquisition by Sequential Analysis
Steven Prestwich
355 - 362
10.3233/FAIA200113
Research Article
Frontiers in Artificial Intelligence and Applications
Volume 325: ECAI 2020
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.

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