<scp>sm</scp> <i>ProbLog: Stable Model Semantics in ProbLog for Probabilistic Argumentation</i>

A Probabilistic Extension of the Stable Model Semantics

We present a probabilistic extension of logic programs under the stable model semantics, inspired by the idea of Markov Logic Networks. The proposed language, called LP, is a generalization of logic programs under the stable model semantics, and as such, embraces the rich body of research in knowledge representation. The language is also a generalization of ProbLog, and is closely related to Ma...

Probabilistic Inductive Querying Using ProbLog

We study how probabilistic reasoning and inductive querying can be combined within ProbLog, a recent probabilistic extension of Prolog. ProbLog can be regarded as a database system that supports both probabilistic and inductive reasoning through a variety of querying mechanisms. After a short introduction to ProbLog, we provide a survey of the different types of inductive queries that ProbLog s...

ProbLog Technology for Inference in a Probabilistic First Order Logic

We introduce First Order ProbLog, an extension of first order logic with soft constraints where formulas are guarded by probabilistic facts. The paper defines a semantics for FOProbLog, develops a translation into ProbLog, a system that allows a user to compute the probability of a query in a similar setting restricted to Horn clauses, and reports on initial experience with inference.

Stable model semantics for founded bounds

Answer Set Programming (ASP) is a powerful form of declarative programming used in areas such as planning or reasoning. ASP solvers enforce stable model semantics, which rule out solutions representing certain kinds of circular reasoning. Unfortunately, current ASP solvers are incapable of solving problems involving cyclic dependencies between multiple integer or continuous quantities effective...

The Stable Model Semantics for Logic Programming