Live briefing
Everything about Stable model semantics
The concept of a stable model, or answer set, is used to define a declarative semantics for logic programs with negation as failure. This is one of several standard approaches to the meaning of negation in logic programming, along with program completion and the well-founded semantics. The stable model semantics is the basis of answer set programming. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Stable model semanticsA 5-minute orientation
- Skim a recent paper: Text semantics to controllable design: A residential layout generation method based on stable diffusion modelInfluential work since 2022
- Pick up a classic book: Neural Information ProcessingThe most-reprinted book on the subject
- Read the landmark paper: Logic programs with stable model semantics as a constraint programming paradigmCited 477 times
- Go sideways: Answer set programmingA closely related topic
- Go sideways: Negation as failureA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Logic programs with stable model semantics as a constraint programming paradigm
Extending and implementing the stable model semantics
Disjunctive Stable Models: Unfounded Sets, Fixpoint Semantics, and Computation
Unfolding partiality and disjunctions in stable model semantics
A new fixpoint semantics for general logic programs compared with the well-founded and the stable model semantics
Disjunctive LP+integrity constraints= stable model semantics
Achieving compositionality of the stable model semantics for smodels programs
Epistemic foundation of stable model semantics
🆕 Influential since 2022
Text semantics to controllable design: A residential layout generation method based on stable diffusion model
The Stable Model Semantics for Higher-Order Logic Programming
smProbLog: Stable Model Semantics in ProbLog for Probabilistic Argumentation
Stable Model Semantics for Description Logic Terminologies
The Stable Model Semantics of Datalog with Metric Temporal Operators
📚 Books worth reading
Neural Information ProcessingTingwen Huang · 2012
Foundations of software science and computation structuresFOSSACS 2000 (2000 Berlin, Germany) · 2000
Database repairing and consistent query answeringLeopoldo Bertossi · 2011- 📕Logic programming and knowledge representationJ. Dix, Luís Moniz Pereira · 1998
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Answer set programming Answer set programming (ASP) is a form of declarative programming oriented towards difficult (primarily NP-hard) search problems. It is based on the stable…
- Negation as failure Negation as failure (NAF, for short) is a non-monotonic inference rule in logic programming, used to derive not p (i.e. that p is assumed not to hold)…
- Syntax and semantics of logic programming Logic programming is a programming paradigm that includes languages based on formal logic, including Datalog and Prolog. This article describes the syntax…
- François Fages François Fages (August 23, 1959) is a French computer scientist known for contributions in the areas of unification theory, rule-based modelling, logic…
- Abductive logic programming Abductive logic programming (ALP) is a high-level knowledge-representation framework that can be used to solve problems declaratively, based on abductive…
- Datalog Datalog is a declarative logic programming language. While it is syntactically a subset of Prolog, Datalog generally uses a bottom-up rather than top-down…
- Vladimir Lifschitz Vladimir Lifschitz (born 30 May 1947) is the Gottesman Family Centennial Professor in Computer Sciences at the University of Texas at Austin. He received…
- Michael Gelfond Michael Gelfond is a Professor in Computer Sciences at Texas Tech University in the United States. He received a degree in mathematics from the Steklov…