Probabilistic logic programs are logic programs in which some of the facts are annotated
with probabilities. This paper investigates how classical inference and learning tasks known …

Handbook of Knowledge Representation describes the essential foundations of Knowledge
Representation, which lies at the core of Artificial Intelligence (AI). The book provides an up …

A recent and effective approach to probabilistic inference calls for reducing the problem to
one of weighted model counting (WMC) on a propositional knowledge base. Specifically, the …

We describe a new algorithm for compiling conjunctive normal form (CNF) into Deterministic
Decomposable Negation Normal (d-DNNF), which is a tractable logical form that permits …

Abstract We propose the Probabilistic Sentential Decision Diagram (PSDD): A complete and
canonical representation of probability distributions defined over the models of a given …

Over the past decade general satisfiability testing algorithms have proven to be surprisingly
effective at solving a wide variety of constraint satisfaction problem, such as planning and …

There has been a long standing division in AI between logical symbolic and probabilistic
reasoning approaches. While probabilistic models can deal well with inherent uncertainty in …

Probabilistic logical languages provide powerful formalisms for knowledge representation
and learning. Yet performing inference in these languages is extremely costly, especially if it …

Probabilistic programming languages (PPLs) are an expressive means of representing and
reasoning about probabilistic models. The computational challenge of probabilistic …

Automation and computer intelligence to support complex human decisions becomes
essential to manage large and distributed systems in the Cloud and IoT era. Understanding …