Proceedings Abstracts of the Twenty-Third International Joint Conference on Artificial Intelligence

Efficient Approach to Solve the Minimal Labeling Problem of Temporal and Spatial Qualitative Constraints / 696
Nouhad Amaneddine, Jean-Fran├žois Condotta, Michael Sioutis

The Interval Algebra (IA) and a subset of the Region Connection Calculus (RCC), namely RCC-8, are the dominant Artificial Intelligence approaches for representing and reasoning about qualitative temporal and topological relations respectively. Such qualitative information can be formulated as a Qualitative Constraint Network (QCN). In this paper, we focus on the minimal labeling problem (MLP) and we propose an algorithm to efficiently derive all the feasible base relations of a QCN. Our algorithm considers chordal QCNs and a new form of partial consistency. Further, the proposed algorithm uses tractable subclasses of relations having a specific patchwork property for which closure under weak composition implies the consistency of the input QCN. Experimentations with QCNs of IA and RCC-8 show the importance and efficiency of this new approach.