Weighted Rules under the Stable Model Semantics

By Joohyung Lee, Yi Wang. 2016

78 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Program Analysis & Verification

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Naser Ahmadi
2. Paolo Papotti
3. Mohammed Saeed
4. Samidh Talsania
5. Preslav Nakov
6. Joohyung Lee
7. Yi Wang
8. Neema Kotonya

In Program Analysis & Verification

1. Paolo Papotti
2. Mohammed Saeed
3. Naser Ahmadi
4. Samidh Talsania
5. Joohyung Lee
6. Preslav Nakov
7. Yi Wang

Citations in the corpus

• RuleBERT: Teaching Soft Rules to Pre-Trained Language Models (2021, methodology): “To model uncertainty, we pick one flavor of probabilistic logic programming languages, LP, for reasoning with soft rules (Lee and Wang, 2016).”
• Explainable Fact Checking with Probabilistic Answer Set Programming (2019, methodology): “To address this third challenge, we use probabilistic answer set programming [14].”
• Computing LPMLN using ASP and MLN solvers* (2017, methodology): “For instance, the “Bird” example from the paper by Lee and Wang (2016) can be represented in the input language of lpmln2asp as follows.”