Abstract
Many real-life problems can be represented as constraint satisfaction problems (CSPs) and then be solved using constraint solvers, in which labelling heuristics are used to fine-tune the performance of the underlying search algorithm. However, few guidelines have been proposed for the application domains of these heuristics. If a mapping between application domains and heuristics is known to the solver, then modellers can — if they wish so — be relieved from figuring out which heuristic to indicate or implement. Instead of inferring the application domains of (known) heuristics, we advocate inferring (known or new) heuristics for application domains. Our approach is to first formalise a CSP application domain as a family of models, so as to exhibit the generic constraint store for all models in that family. Second, family-specific labelling heuristics are inferred by analysing the interaction of a given search algorithm with this generic constraint store. We illustrate our approach on a domain of subset problems.
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Kiziltan, Z., Flener, P., Hnich, B. (2001). Towards Inferring Labelling Heuristics for CSP Application Domains. In: Baader, F., Brewka, G., Eiter, T. (eds) KI 2001: Advances in Artificial Intelligence. KI 2001. Lecture Notes in Computer Science(), vol 2174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45422-5_20
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DOI: https://doi.org/10.1007/3-540-45422-5_20
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