Current literature offers a number of different approaches to what could generally be called
“probabilistic logic programming”. These are usually based on Horn clauses. Here, we …

This paper presents Abduction and Argumentation as two principled forms for reasoning,
and fleshes out the fundamental role that they can play within Machine Learning. It reviews …

ProbLog is a probabilistic framework that extends Prolog with probabilistic facts. To compute
the probability of a query, the complete SLD proof tree of the query is collected as a sum of …

The pharmaceutical industry, consumer protection groups, users of medications and
government oversight agencies are all strongly interested in identifying adverse reactions to …

PRISM is an extension of Prolog with probabilistic predicates and built-in support for
expectation-maximization learning. Constraint Handling Rules (CHR) is a high-level …

PRISM is a probabilistic extension of Prolog. It is a high level language for probabilistic
modeling capable of learning statistical parameters from observed data. After reviewing it …

We propose a differentiable framework for logic program inference as a step toward
realizing flexible and scalable logical inference. The basic idea is to replace symbolic …

Programs for gene prediction in computational biology are examples of systems for which
the acquisition of authentic test data is difficult as these require years of extensive research …

ProbLog is a probabilistic framework that extends Prolog with probabilistic facts. Inference in
ProbLog is based on calculations over the SLD proof tree of a query. Tabling is a well known …

In this paper, we describe recent attempts to incorporate learning into logic programs as a
step toward adaptive software that can learn from an environment. Although there are a …