Neuro-symbolic AI is an emerging subfield of Artificial Intelligence that brings together two
hitherto distinct approaches.” Neuro” refers to the artificial neural networks prominent in …

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 …

We present and evaluate a new compiler, called D4, targeting the Decision-DNNF
language. As the state-of-the-art compilers C2D and Dsharp targeting the same language …

Given a Boolean formula F, the problem of model counting, also referred to as# SAT, seeks
to compute the number of solutions of F. Model counting is a fundamental problem with a …

One of the key purposes of eXplainable AI (XAI) is to develop techniques for understanding
predictions made by Machine Learning (ML) models and for assessing how much reliable …

Logic locking has recently been proposed as a solution for protecting gatelevel
semiconductor intellectual property (IP). However, numerous attacks have been mounted on …

Many computational problems in modern society account to probabilistic reasoning,
statistics, and combinatorics. A variety of these real-world questions can be solved by …

Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of
activity, with many proposals for languages and algorithms for inference and learning. This …

Feature modeling is widely used to systematically model features of variant-rich software
systems and their dependencies. By translating feature models into propositional formulas …

We consider three modern roles for logic in artificial intelligence, which are based on the
theory of tractable Boolean circuits:(1) logic as a basis for computation,(2) logic for learning …