LAGER

Abstract

Generalization is an important ability that allows humans to tackle complex problems by identifying common problem structures and the essential details to do reasoning. Although this ability comes naturally to humans, providing it in AI systems turned out to be a challenge. Constructing “good” abstractions over the problems from a few instances is key to finding generalized solutions that work for all potential instances with similar structure. The obtained generalized solutions can also be used as a guide in searching for a solution to the original problem.

Machine learning methods excel at certain generalization tasks, but the generated models lack transparency and guarantees of the generalization. In Symbolic AI, different approaches have been proposed for simplification, abstraction of details, and generalization, usually focusing on concrete domains. However a general theoretical understanding of the generalization ability, and more importantly, methods to compute abstractions, especially those that are domain-independent, are still lacking.

This project takes on this challenge in the context of answer set programming (ASP), one of the premier formalisms of symbolic AI. ASP is a modern declarative problem solving approach which, thanks to its efficient solvers and expressive language, is increasingly popular for solving a variety of problems in AI and Computer Science. With a strong team, we aim to establish theoretical foundations for generalized reasoning, and to develop methods for learning useful abstractions over programs that allow one to obtain generalized solutions. The experimental implementations will be evaluated with respect to performance and solution quality on industrial-type problems and generalization tasks. Our results will pave the way for a new generation of solvers with generalization abilities that are applicable to various problems.

People

The LAGER project is carried out by the following core team:

• Thomas Eiter (co-PI)
• Nysret Musliu (co-PI)
• Zeynep G. Saribatur (PI)

Open Positions

We are looking for one Postdoc and two PhD students to join our project on learning abstractions for generalized reasoning, specifically focusing on Answer Set Programming (ASP).

The project tackles the challenge of equipping AI systems with the human-like ability to generalize, such as identifying common problem structures and generating solutions that work for diverse instances, for solving real-world problems and generalization benchmarks. We will establish theoretical foundations for generalized reasoning and develop domain-independent methods for learning useful abstractions over ASP programs.

Postdoc Position

Candidates should have a strong background in logic-based AI, answer set programming, inductive logic programming or machine learning.

The Postdoc position is available from January 1st, 2026 and is for 2 years.

Interested candidates should send a research statement, a complete CV, and two recommendation letters, sent separately by each reference person, to Zeynep G. Saribatur.

The reference letters can be sent within two weeks from submission.

PhD Positions

Candidates should have a strong background in symbolic AI and good programming skills.

One PhD position is available from January 1st, 2026, and the other position is from July 1st, 2026.

Interested candidates should send a research statement, a complete CV, transcripts of completed studies, abstract of the applicant's MSc or BSc thesis, and two recommendation letters, sent separately by each reference person, to Zeynep G. Saribatur.

The reference letters can be sent within two weeks from submission.

The positions are affiliated with the Knowledge-Based Systems Group (KBS) and the Database and AI Group (DBAI) of the Institute of Logic and Computation at TU Wien.

Applications will be processed on a regular basis and continue until the positions are filled.

In case of questions, do not hesitate to contact us.

News

• Our project has been accepted for funding by the WWTF! We are proud to be a part of the WWTF Funding Fever at TU Wien.