Efficient Approach to Solve the Minimal Labeling Problem of Temporal and Spatial Qualitative Constraints

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 QCN s of IA and RCC-8 show the importance and efficiency of this new approach.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-00873047
Contributor : Francois Chevallier <>
Submitted on : Tuesday, October 15, 2013 - 9:23:13 AM
Last modification on : Thursday, March 21, 2019 - 2:46:59 PM

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  • HAL Id : hal-00873047, version 1

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Nouhad Amaneddine, Jean-Francois Condotta, Michael Sioutis. Efficient Approach to Solve the Minimal Labeling Problem of Temporal and Spatial Qualitative Constraints. 23th International Joint Conference on Artificial Intelligence (IJCAI'13), Aug 2013, Beijing, China. pp.696-702. ⟨hal-00873047⟩

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