Abstract
Minimal Unsatisfiable Sets (MUSes) are useful in a number of applications. However, in general there are many different MUSes, and each application might have different preferences over these MUSes. Typical Muser systems produce a single MUS without much control over which MUS is generated. In this paper we describe an algorithm that can efficiently compute a collection of MUSes, thus presenting an application with a range of choices. Our algorithm improves over previous methods for finding multiple MUSes by computing its MUSes incrementally. This allows it to generate multiple MUSes more efficiently; making it more feasible to supply applications with a collection of MUSes rather than just one.
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Version 1.1, downloaded from https://sun.iwu.edu/~mliffito/marco/.
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Bacchus, F., Katsirelos, G. (2016). Finding a Collection of MUSes Incrementally. In: Quimper, CG. (eds) Integration of AI and OR Techniques in Constraint Programming. CPAIOR 2016. Lecture Notes in Computer Science(), vol 9676. Springer, Cham. https://doi.org/10.1007/978-3-319-33954-2_3
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