Weighted Rules under the Stable Model Semantics
For researchers in nonmonotonic reasoning and probabilistic logic, this work extends answer set programming with probabilistic capabilities, though it is incremental over existing approaches like Markov Logic.
The paper introduces weighted rules under the stable model semantics, enabling probabilistic reasoning and inconsistency resolution in answer set programs, with formal comparisons to related formalisms.
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.