@inproceedings {28050,
	title = {GEQCA: Generic Qualitative Constraint Acquisition},
	journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
	volume = {36},
	year = {2022},
	month = {06/2022},
	pages = {3690-3697},
	publisher = {AAAI},
	abstract = {Many planning, scheduling or multi-dimensional packing problems involve the design of subtle logical combinations of temporal or spatial constraints. On the one hand, the pre- cise modelling of these constraints, which are formulated in various relation algebras, entails a number of possible logical combinations and requires expertise in constraint-based mod- elling. On the other hand, active constraint acquisition (CA) has been used successfully to support non-experienced users in learning conjunctive constraint networks through the gen- eration of a sequence of queries. In this paper, we propose GEQCA, which stands for Generic Qualitative Constraint Acquisition, an active CA method that learns qualitative con- straints via the concept of qualitative queries. GEQCA com- bines qualitative queries with time-bounded path consistency (PC) and background knowledge propagation to acquire the qualitative constraints of any scheduling or packing prob- lem. We prove soundness, completeness and termination of GEQCA by exploiting the jointly exhaustive and pairwise disjoint property of qualitative calculus and we give an ex- perimental evaluation that shows (i) the efficiency of our ap- proach in learning temporal constraints and, (ii) the use of GEQCA on real scheduling instances.},
	doi = {https://doi.org/10.1609/aaai.v36i4.20282},
	url = {https://ojs.aaai.org/index.php/AAAI/article/view/20282},
	author = {Mohamed Bachir {Belaid} and Belmecheri, Nassim and Gotlieb, Arnaud and Lazaar, Nadjib and Spieker, Helge}
}
