The field of probabilistic logic programming (PLP) focuses on integrating probabilistic
models into programming languages based on logic. Over the past 30 years, numerous …

We introduce a declarative differentiable programming framework, based on the language of
Lifted Relational Neural Networks, where small parameterized logic programs are used to …

To solve hard problems, AI relies on a variety of disciplines such as logic, probabilistic
reasoning, machine learning and mathematical programming. Although it is widely accepted …

Probabilistic logic programming is an effective formalism for encoding problems
characterized by uncertainty. Some of these problems may require the optimization of …

Purpose In this paper, the authors consider how qualitative research techniques that are
used in applied psychology to understand a person's feelings and needs provides a means …

Abstract Lifted Relational Neural Networks (LRNNs) were introduced in 2015 [1] as a
framework for combining logic programming with neural networks for efficient learning of …

The journey captured by this dissertation centers around knowledge compilation, model
counting, and their role within state-of-the-art inference algorithms for probabilistic logic …

In the recent years, we have seen tremendous resurgence of neural networks, applied with
great success in highly diverse domains, ranging from speech recognition to game playing …

Inductive Logic Programming: Challenges Page 1 Inductive Logic Programming:
Challenges Katsumi Inoue National Institute of Informatics Tokyo, Japan inoue@nii.ac.jp …

Abstract Symbolic Artificial Intelligence has been considered''Good Old-Fashioned Artificial
Intelligence''since it usually represents knowledge through explicit symbols, such as first …