SHAP explanations are a popular feature-attribution mechanism for explainable AI. They
use game-theoretic notions to measure the influence of individual features on the prediction …

An intelligent agent interacting with the real world will encounter individual people, courses,
test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of …

A multitude of different probabilistic programming languages exists today, all extending a
traditional programming language with primitives to support modeling of complex, structured …

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 …

Lifted graphical models provide a language for expressing dependencies between different
types of entities, their attributes, and their diverse relations, as well as techniques for …

Weighted model counting (WMC) is a well-known inference task on knowledge bases, and
the basis for some of the most efficient techniques for probabilistic inference in graphical …

Probabilistic data is motivated by the need to model uncertainty in large databases. Over the
last twenty years or so, both the Database community and the AI community have studied …

The field of Probabilistic Logic Programming (PLP) has seen significant advances in the last
20 years, with many proposals for languages that combine probability with logic …

The FO Model Counting problem (FOMC) is the following: given a sentence Φ in FO and a
number n, compute the number of models of Φ over a domain of size n; the Weighted variant …

The combination of logic programming and probability has proven useful for modeling
domains with complex and uncertain relationships among elements. Many probabilistic logic …