Abstract
Classical constraint satisfaction problems (CSPs) provide an expressive formalism for describing and solving many real-world problems. However, classical CSPs prove to be restrictive in situations where uncertainty, fuzziness, probability or optimisation are intrinsic. Soft constraints alleviate many of the restrictions which classical constraint satisfaction impose; in particular, soft constraints provide a basis for capturing notions such as vagueness, uncertainty and cost into the CSP model. We focus on the semiring-based approach to soft constraints. In this paper we present a new evaluation-based scheme for implementing meta-constraints, which can be applied to any existing implementation to improve its run-time performance.
This work has received support from Enterprise Ireland under their Basic Research Grant Scheme (Grant Number SC/02/289).
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Kelleher, J., O’Sullivan, B. (2004). Evaluation-Based Semiring Meta-constraints. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_19
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DOI: https://doi.org/10.1007/978-3-540-24694-7_19
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