We’re excited to announce that Alexander Beiser, Markus Hecher (CNRS), and Stefan Woltran won the Best Student Paper award at ICLP 2025 for their Paper “Automated Hybrid Grounding Using Structural and Data-Driven Heuristics”!

The paper advances the “grounding bottleneck” in Answer Set Programming (ASP), a problem that slows down computation when abstract logic rules are converted into a form a computer can process. This issue can be reduced by using two methods: bottom-up grounding, which processes all rules at once, and body-decoupled grounding, which breaks rules into smaller parts for more flexibility and faster processing. The challenge, however, is knowing when to use each method. The authors introduce an automated approach that helps decide which method to use based on the structure of the rules and the data involved. By making this decision automatically, the algorithm improves the overall efficiency of the grounding process.

The International Conference on Logic Programming (ICLP), which took place in mid-September, is the leading academic conference for research in logic programming. It spans topics such as formal and operational semantics, non-monotonic reasoning, knowledge representation, and complexity. It also addresses program analysis and optimization, focusing on verification, debugging, and logic-based validation. Held annually and organized by the Association for Logic Programming (ALP), the conference features peer-reviewed papers, with post-proceedings published in the Theory and Practice of Logic Programming (TPLP) journal. Acceptance for papers at ICLP is highly selective, with only about 20% of submissions accepted for publication in the TPLP journal.

Congratulations to Alexander, Markus, and Stefan on this outstanding achievement!

Abstract

The grounding bottleneck poses one of the key challenges that hinders the widespread adoption of Answer Set Programming in industry. Hybrid Grounding is a step in alleviating the bottleneck by combining the strength of standard bottom-up grounding with recently proposed techniques where rule bodies are decoupled during grounding. However, it has remained unclear when hybrid grounding shall use body-decoupled grounding and when to use standard bottom-up grounding. In this paper, we address this issue by developing automated hybrid grounding: we introduce a splitting algorithm based on data-structural heuristics that detects when to use body-decoupled grounding and when standard grounding is beneficial. We base our heuristics on the structure of rules and an estimation procedure that incorporates the data of the instance. The experiments conducted on our prototypical implementation demonstrate promising results, which show an improvement in hard-to-ground scenarios, whereas on hard-to-solve instances, we approach state-of-the-art performance.

About the authors

Alexander Beiser is a PhD student at TU Wien Informatics, developing neurosymbolic and explainable Artificial Intelligence (AI) for Air Traffic Management (ATM). Alexander is supervised by Stefan Woltran and Nysret Musliu in collaboration with Frequentis. His work aims to integrate trustworthy AI into ATM to boost efficiency and safety amid rising traffic and staffing constraints. Alexander holds a BSc in Software & Information Engineering (2023) and an MSc in Logic & Computation (2025) from TU Wien. Before his PhD, he worked on Answer Set Programming (ASP) and held research roles at the University of Potsdam (2022, 2023) and TU Wien’s Research Unit Databases and Artificial Intelligence. In 2024, he spent a semester at EPFL (Lausanne) and was selected for Summer@EPFL 2024, a highly competitive research fellowship. He has presented his work at IJCAI (2024) and has publications at top-tier venues in AI, including IJCAI’24 and ICLP’24, with accepted papers for ICLP’25 and KR’25.

Markus Hecher completed his PhD jointly at TU Wien and the University of Potsdam (Germany). His research focuses on structural graph parameters, computational complexity, quantitative neurosymbolic reasoning, and precise runtime bounds—bridging both theoretical and practical perspectives. He is currently a CNRS researcher at the CRIL laboratory in France. Prior to this, he spent two years as a Postdoctoral Fellow at MIT in the United States, working closely with Erik Demaine through an FWF Schrödinger fellowship. To date, Markus has co-authored more than 90 research publications across several aspects of computer science. His contributions have been recognized with multiple distinctions, including PhD thesis awards, best paper awards, early career awards, and competition awards.

Stefan Woltran is a full professor of Foundations of Artificial Intelligence at the TU Wien and head of the Research Unit Databases and AI. His research focuses on problems in knowledge representation and reasoning, argumentation, complexity analysis in AI, and logic programming. In 2013, he held a deputy professorship at Leipzig University and received the prestigious START award from the Austrian Science Fund (FWF). He acted as PC Chair for the 10th International Symposium on Foundations of Information and Knowledge Systems (FoIKS’18) and for the 15th International Conference on Logic Programming and Non-monotonic Reasoning (LPNMR’19). He has led several research projects funded by FWF and Vienna Science and Technology Fund (WWTF). Since 2018, he has been a fellow of the European Association for Artificial Intelligence. Since 2020, he has served as Vice dean of Academic Affairs of the Faculty of Informatics at TU Wien.

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