Efficient approach to solve the minimal labeling problem of temporal and spatial qualitative constraints

  • Authors:

  • Nouhad Amaneddine;Jean-François Condotta;Michael Sioutis

  • Affiliations:

  • Arab Open University, Lebanon;Université Lille-Nord de France, CRIL-CNRS, Lens, France;Université Pierre et Marie Curie, Paris, France

  • Venue:
  • IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
  • Year:
  • 2013

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Abstract

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 which we define as G♦-consistency. Further, the proposed algorithm uses tractable subclasses of relations having a specific patchwork property for which ⋄-consistency implies the consistency of the input QCN. Experimentations with QCNs of IA and RCC-8 show the importance and efficiency of this new approach.