Abstract
The Interval Algebra (IA) and a subset of the Region Connection Calculus, 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 framework, one of the main tasks is to compute the path consistency of a given QCN. We propose a new algorithm that applies path consistency in a vertex incremental manner. Our algorithm enforces path consistency on an initial path consistent QCN augmented by a new temporal or spatial entity and a new set of constraints, and achieves better performance than the state-of-the-art approach. We evaluate our algorithm experimentally with QCNs of RCC-8 and show the efficiency of our approach.
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). Vertex Incremental Path Consistency for Qualitative Constraint Networks. In: Likas, A., Blekas, K., Kalles, D. (eds) Artificial Intelligence: Methods and Applications. SETN 2014. Lecture Notes in Computer Science(), vol 8445. Springer, Cham. https://doi.org/10.1007/978-3-319-07064-3_39
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DOI: https://doi.org/10.1007/978-3-319-07064-3_39
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