Model generation is an important formal technique for finding interesting instances of computationally hard problems. In this paper we study model generation over Horn logic under the closed world assumption extended with stratified negation. We provide a novel three-stage algorithm that solves this problem: First, we reduce the relevant Horn clauses to a set of non-monotonic predicates. Second, we apply a fixed-point procedure to these predicates that reveals candidate solutions to the model generation problem. Third, we encode these candidates into a satisfiability problem that is evaluated with a state-of-the-art SMT solver. Our algorithm is implemented, and has been successfully applied to key problems arising in model-based design. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Jackson, E. K., & Schulte, W. (2008). Model generation for horn logic with stratified negation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5048 LNCS, pp. 1–20). https://doi.org/10.1007/978-3-540-68855-6_1
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