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Model Generation for Horn Logic with Stratified Negation

  • Ethan K. Jackson
  • Wolfram Schulte
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5048)

Abstract

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.

Keywords

Function Symbol Horn Clause Closed World Ground Term Proof Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© IFIP International Federation for Information Processing 2008

Authors and Affiliations

  • Ethan K. Jackson
    • 1
  • Wolfram Schulte
    • 1
  1. 1.Microsoft ResearchRedmondUSA

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