@misc{16219,
  author = {Mohamed Belaid and Nassim Belmecheri and Arnaud Gotlieb and Nadjib Lazaar and Helge Spieker},
  title = {GEQCA: Generic Qualitative Constraint Acquisition},
  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.},
  year = {2022},
  journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume = {36},
  pages = {3690-3697},
  month = {06/2022},
  publisher = {AAAI},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/20282},
  doi = {https://doi.org/10.1609/aaai.v36i4.20282},
}