While large language models (LLMs), such as GPT-3, appear to be robust and general, their
reasoning ability is not at a level to compete with the best models trained for specific natural …

Driven by deep neural networks (DNN), the recent development of computer vision makes
vision sensors such as stereo cameras and Lidars ubiquitous in autonomous cars, robotics …

We present plingo, an extension of the ASP system clingo with various probabilistic
reasoning modes. Plingo is centered upon LP MLN, a probabilistic extension of ASP based …

This tutorial presents a statistical relational extension of the answer set programming
language called LP MLN, which incorporates the concept of weighted rules into the stable …

We present plingo, an extension of the answer set programming (ASP) system clingo that
incorporates various probabilistic reasoning modes. Plingo is based on to ProbLog. This …

Solution counting or solution space quantification (means volume computation and volume
estimation) for linear constraints (LCs) has found interesting applications in various fields …

One of the challenges in Artificial Intelligence (AI) is to integrate fast, automatic, and intuitive
System-1 thinking with slow, deliberate, and logical System-2 thinking. While deep learning …

LPMLN is a probabilistic extension of answer set programs with the weight scheme adapted
from Markov Logic. We study the concept of strong equivalence in LPMLN, which is a useful …

We present the Credal-FOIL system for structure learning of probabilistic logic programs
under the credal semantics. The credal semantics is a generalisation of the distribution …

Abstract Probabilistic Answer Set Programming (PASP) is a powerful formalism that allows
to model uncertain scenarios with answer set programs. One of the possible semantics for …