Daniel Gnad

Assistant Professor

My research interests are in the fields of artificial intelligence planning and model checking.

Artificial intelligence planning and model checking

I am assistant professor at the Artificial Intelligence and Integrated Computer Systems division (AIICS). I am working on state space search methods such as decoupled state space search and have some experience in developing domain-independent heuristics such as red-black heuristics. More recently, I got interested in the grounding process that most planning systems perform as preprocessing. Together with my co-authors, I work on partial grounding methods that try to tackle the exponential blow-up that can occur during grounding by leveraging machine learning techniques.

CV in brief

Short bio

I did my studies in Computer Science at Saarland University. After finishing my MSc. degree, I stayed on as a PhD student in the group of Prof. Jörg Hoffmann. In 2022, I joined the RLPLab at Linköping University as a postdoctoral researcher, where I became assistant professor in 2023.

Awards

Dr.-Eduard-Martin-Preis for the best dissertation of the Faculty for Mathematics and Computer Science in 2021 awarded by Saarland University.
SoCS 2022 Best Paper Award at the 15th Annual Symposium on Combinatorial Search for the paper Additive Pattern Databases for Decoupled Search

ICAPS 2022 Best Dissertation Award for the doctoral thesis Star-Topology Decoupled State-Space Search in AI Planning and Model Checking at the 32th International Conference on Automated Planning and Scheduling.
Nominated for the Dissertation Award 2021 of the German Society for Computer Science (GI). This is a national award (joint with Switzerland and Austria) for the best dissertation in the field of Computer Science. Nominated by Saarland University.
SPIN 2018 Best Paper Award for the paper Star-Topology Decoupling in SPIN at the 25th International Symposium on Model Checking of Software.

Runner-Up, Deterministic Sequential Agile Track for the planning system Saarplan at the 9th International Planning Competition (IPC 2018) at ICAPS 2018.
Runner-Up, Deterministic Sequential Bounded-Cost Track for the planning system Saarplan at the 9th International Planning Competition (IPC 2018) at ICAPS 2018.
Special Recognition for the planning system Saarplan for solving the highest number of problems of all planners in both the Agile and the Satisficing track at the 9th International Planning Competition (IPC 2018) at ICAPS 2018.

Publications

2025

David Speck, Markus Hecher, Daniel Gnad, Johannes Klaus Fichte, Augusto B. Corrêa (2025) Counting and Reasoning with Plans Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence (AAAI'25) (Conference paper)

2024

David Speck, Daniel Gnad (2024) Decoupled Search for the Masses: A Novel Task Transformation for Classical Planning Proceedings of the Thirty-Fourth International Conference on Automated Planning and Scheduling (Conference paper)

Continue to DOI

2023

Alexander Shleyfman, Daniel Gnad, Peter Jonsson (2023) Structurally Restricted Fragments of Numeric Planning - a Complexity Analysis THIRTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 37 NO 10, p. 12112-12119 (Conference paper)

Continue to DOI

Daniel Gnad, Malte Helmert, Peter Jonsson, Alexander Shleyfman (2023) Planning over Integers: Compilations and Undecidability

David Speck, Paul Höft, Daniel Gnad, Jendrik Seipp (2023) Finding Matrix Multiplication Algorithms with Classical Planning Proceedings of the 33rd International Conference on Automated Planning and Scheduling (ICAPS 2023), p. 411-416 (Conference paper)

News

Best paper award in automated planning

David Speck and Daniel Gnad at the Department of Computer and Information Science have won the Best Paper Award at the Conference ICAPS 2024, one of the most important conferences for automated planning, a topic in artificial intelligence.

About the division

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Artificial Intelligence and Integrated Computer Systems (AIICS)

The AIICS division is part of the Department of Computer and Information Science. The division's main focus is research and teaching in artificial intelligence, its theoretical foundations and its applications.

Colleagues at AIICS