Abstract
The Interval Algebra (IA) and a fragment 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. In this framework, one of the main tasks is to compute the path consistency of a given Qualitative Constraint Network (QCN). We concentrate on the partial path consistency checking problem problem of a QCN, i.e., the path consistency enforced on an underlying chordal constraint graph of the QCN, and propose an algorithm for maintaining or enforcing partial path consistency for growing constraint networks, i.e., networks that grow with new temporal or spatial entities over time. We evaluate our algorithm experimentally with QCNs of IA and RCC-8 and obtain impressive results.
This work was funded by a PhD grant from Université d’Artois and region Nord-Pas-de-Calais.
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Sioutis, M., Condotta, JF. (2014). Incrementally Building Partially Path Consistent Qualitative Constraint Networks. In: Agre, G., Hitzler, P., Krisnadhi, A.A., Kuznetsov, S.O. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2014. Lecture Notes in Computer Science(), vol 8722. Springer, Cham. https://doi.org/10.1007/978-3-319-10554-3_10
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DOI: https://doi.org/10.1007/978-3-319-10554-3_10
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