Thomas Eiter

Meta-Interpretive Learning Using HEX-Programs

2019

Meta-Interpretive Learning (MIL) is a recent approach for Inductive Logic Programming (ILP) implemented in Prolog. Alternatively, MIL-problems can be solved by using Answer Set Programming (ASP), which may result in performance gains due to efficient …

Leveraging Neurosymbolic AI for Slice Discovery

2026 · Neurosymbolic Artificial Intelligence

While remarkable recent developments in deep neural networks have significantly contributed to advancing the state-of-the-art in computer vision (CV), several studies have also shown their limitations and defects. In particular, CV models often make systematic …

Answer-Set-Programming-Based Abstractions for Reinforcement Learning

2026 · Theory and Practice of Logic Programming

Abstract Reinforcement Learning (RL) enables autonomous agents to learn policies from experience, but realistic problems often involve enormous state spaces, making learning and generalisation challenging. Abstraction and approximation are therefore essential. Relational Reinforcement Learning (RRL) …