Abstract
We focus on the recently introduced problem of maximizing the number of satisfied constraints in a qualitative constraint network (\(\mathsf {QCN}\)), called the \(\mathsf {MAX}\text{- }\mathsf {QCN}\) problem. We present a particular local search method for solving the \(\mathsf {MAX}\text{- }\mathsf {QCN}\) problem of a given \(\mathsf {QCN}\), which involves first obtaining a partial scenario \(\mathcal {S}\) of that \(\mathsf {QCN}\) and then exploring neighboring scenarios that are obtained by disconnecting a variable of \(\mathcal {S}\) and repositioning it appropriately. The experimentation that we have conducted shows the interest of our approach for maximizing satisfiability in qualitative spatial and temporal constraint networks.
This work is partially supported by PHC-UTIQUE program (project RESCUESYS-34942VF) managed by the CMCU.
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Condotta, JF., Mensi, A., Nouaouri, I., Sioutis, M., Ben Saïd, L. (2016). Local Search for Maximizing Satisfiability in Qualitative Spatial and Temporal Constraint Networks. In: Dichev, C., Agre, G. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016. Lecture Notes in Computer Science(), vol 9883. Springer, Cham. https://doi.org/10.1007/978-3-319-44748-3_24
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