• Media type: Text; E-Article; Electronic Conference Proceeding
  • Title: Collective Singleton-Based Consistency for Qualitative Constraint Networks
  • Contributor: Sioutis, Michael [Author]; Paparrizou, Anastasia [Author]; Condotta, Jean-François [Author]
  • Published: Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2017
  • Language: English
  • DOI: https://doi.org/10.4230/LIPIcs.TIME.2017.19
  • Keywords: minimal labeling pr ; qualitative spatial and temporal reasoning ; partial singleton path-consistency ; Qualitative constraint network ; local consistency
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  • Description: Partial singleton closure under weak composition, or partial singleton (weak) path-consistency for short, is essential for approximating satisfiability of qualitative constraints networks. Briefly put, partial singleton path-consistency ensures that each base relation of each of the constraints of a qualitative constraint network can define a singleton relation in the corresponding partial closure of that network under weak composition, or in its corresponding partially (weak) path-consistent subnetwork for short. In particular, partial singleton path-consistency has been shown to play a crucial role in tackling the minimal labeling problem of a qualitative constraint network, which is the problem of finding the strongest implied constraints of that network. In this paper, we propose a stronger local consistency that couples partial singleton path-consistency with the idea of collectively deleting certain unfeasible base relations by exploiting singleton checks. We then propose an efficient algorithm for enforcing this consistency that, given a qualitative constraint network, performs fewer constraint checks than the respective algorithm for enforcing partial singleton path-consistency in that network. We formally prove certain properties of our new local consistency, and motivate its usefulness through demonstrative examples and a preliminary experimental evaluation with qualitative constraint networks of Interval Algebra.
  • Access State: Open Access