Weighted Rules under the Stable Model Semantics

We introduce the concept of weighted rules under the stable model semantics following the log-linear models of Markov Logic. This provides versatile methods to overcome the deterministic nature of the stable model semantics, such as resolving inconsistencies in answer set programs, ranking stable models, associating probability to stable models, and applying statistical inference to computing weighted stable models. We also present formal comparisons with related formalisms, such as answer set programs, Markov Logic, ProbLog, and P-log.

• NS
  National Science Foundation

  Awards: IIS-1319794, 1526301, IIS-1526301, 1319794
• MO
  Ministry of Science, ICT and Future Planning

  Award: 10044494
• IF
  Institute for Information and Communications Technology Promotion