Constraint acquisition and the data collection bottleneck
Prestwich, Steven D.
The field of constraint acquisition (CA) aims to remove the “modelling bottleneck” by learning constraints from examples. However, it gives rise to a “data collection bottleneck” as humans must prepare a suitable (labelled) dataset. A recently published paper described an unsupervised CA method called MineAcq that can learn standard CA benchmarks. In this paper we summarise the results, and apply MineAcq to a new, noisy, unlabelled dataset that was not designed for CA.
Constraint acquisition (CA) , Artificial Intelligence (AI) , Constraint satisfaction problem (CSP) , MineAcq
Prestwich, S. D. (2022) 'Constraint Acquisition and the Data Collection Bottleneck', AAAI-22: The Thirty-Sixth AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada, 22 Feb - 1 Mar.