5 papers
Distilling Answer-Set Programming Rules from LLMs for Neurosymbolic Visual Question Answering
Thomas Eiter, Nelson Higuera Ruiz, Johannes Oetsch
Visual Question Answering (VQA) is the task of answering questions about images, requiring the integration of multimodal input and reasoning. Modular approaches that incorporate lo…
Answer-Set-Programming-based Abstractions for Reinforcement Learning
Rafael Bankosegger, Thomas Eiter, Johannes Oetsch
Reinforcement Learning (RL) enables autonomous agents to learn policies from experience, but realistic problems often involve enormous state spaces, making learning and generalisat…
Visual Perceptual to Conceptual First-Order Rule Learning Networks
Kun Gao, Davide SoldÃ, Thomas Eiter +1
Learning rules plays a crucial role in deep learning, particularly in explainable artificial intelligence and enhancing the reasoning capabilities of large language models. While e…
ASP-FZN: A Translation-based Constraint Answer Set Solver
Thomas Eiter, Tobias Geibinger, Tobias Kaminski +2
We present the solver asp-fzn for Constraint Answer Set Programming (CASP), which extends ASP with linear constraints. Our approach is based on translating CASP programs into the s…
Visual Graph Question Answering with ASP and LLMs for Language Parsing
Jakob Johannes Bauer, Thomas Eiter, Nelson Higuera Ruiz +1
Visual Question Answering (VQA) is a challenging problem that requires to process multimodal input. Answer-Set Programming (ASP) has shown great potential in this regard to add int…