We present NeurASP, a simple extension of answer set programs by embracing neural
networks. By treating the neural network output as the probability distribution over atomic …

This article describes REBA, a knowledge representation and reasoning architecture for
robots that is based on tightly-coupled transition diagrams of the domain at two different …

Abstract Probabilistic Answer Set Programming (PASP) combines rules, facts, and
independent probabilistic facts. We argue that a very useful modeling paradigm is obtained …

Probabilistic reasoning, parameter learning, and most probable explanation inference for
answer set programming have recently received growing attention. They are only some of …

Many important problems in AI, among them# SAT, parameter learning and probabilistic
inference go beyond the classical satisfiability problem. Here, instead of finding a solution …

LPMLN is a recent addition to probabilistic logic programming languages. Its main idea is to
overcome the rigid nature of the stable model semantics by assigning a weight to each rule …

Quantitative extensions of logic programming often require the solution of so called second
level inference tasks, that is, problems that involve a third operation, such as maximization or …

In this expository article we highlight the relevance of explanations for artificial intelligence,
in general, and for the newer developments in explainable AI, referring to origins and …

We propose answer-set programs that specify and compute counterfactual interventions on
entities that are input on a classification model. In relation to the outcome of the model, the …

Knowledge Representation and Reasoning is a central, longstanding, and active area of
Artificial Intelligence. Over the years it has evolved significantly; more recently it has been …