Awards

• Quality Champion Award for submitting reviews that exceed expectations (ECAI 2023).
• Winner, Deterministic Optimal Track for the Ragnarok planner with Dominik Drexler, Daniel Gnad, Paul Höft, Jendrik Seipp, and David Speck at the 10th International Planning Competition (IPC 2023) at (ICAPS 2023).
• ICAPS 2022 Best Paper Award for the paper Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limits with Blai Bonet and Hector Geffner at the 32th International Conference on Automated Planning and Scheduling (ICAPS 2022).
• IJCAI 2021 Distinguished Paper Award for the paper Learning Generalized Unsolvability Heuristics for Classical Planning (PDF) with Jendrik Seipp and Guillem Francès at the 30th International Joint Conference on Artificial Intelligence (IJCAI 2021).

Awards

• Winner, Deterministic Optimal Track for the Ragnarok planner with Dominik Drexler, Daniel Gnad, Paul Höft, Jendrik Seipp, and David Speck at the 10th International Planning Competition (IPC 2023) at ICAPS 2023.
• ICAPS 2022 Best Paper Award for the paper Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limits with Blai Bonet and Hector Geffner at the 32nd International Conference on Automated Planning and Scheduling (ICAPS 2022).
• IJCAI 2021 Distinguished Paper Award for the paper Learning Generalized Unsolvability Heuristics for Classical Planning with Guillem Francès and Jendrik Seipp at the 30th International Joint Conference on Artificial Intelligence (IJCAI 2021).

Publications

2024

• Dominik Drexler, Simon Ståhlberg, Blai Bonet and Hector Geffner.
  Symmetries and Expressive Requirements for Learning General Policies.
  In Proceedings of the Twenty-First International Conference on Principles of Knowledge Representation and Reasoning (KR 2024). 2024.
  paper slides citation

• Martin Funkquist, Simon Ståhlberg and Hector Geffner.
  Learning to Ground Existentially Quantified Goals.
  In Proceedings of the Twenty-First International Conference on Principles of Knowledge Representation and Reasoning (KR 2024). 2024.
  paper code citation

• Dominik Drexler, Simon Ståhlberg, Blai Bonet and Hector Geffner.
  Equivalence-Based Abstractions for Learning General Policies.
  In ICAPS 2024 Workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL). 2024.
  paper slides citation

2023

• Simon Ståhlberg.
  Lifted Successor Generation by Maximum Clique Enumeration.
  In Proceedings of the 26th European Conference on Artificial Intelligence (ECAI 2023), pp. 2194–2201. 2023.
  paper citation

• Simon Ståhlberg, Blai Bonet and Hector Geffner.
  Learning General Policies with Policy Gradient Methods.
  In Proceedings of the Twentieth International Conference on Principles of Knowledge Representation and Reasoning (KR 2023), pp. 647–657. 2023.
  paper citation

• Dominik Drexler, Daniel Gnad, Paul Höft, Jendrik Seipp, David Speck and Simon Ståhlberg.
  Ragnarok.
  In Tenth International Planning Competition (IPC-10): Planner Abstracts. 2023.
  paper citation

2022

• Simon Ståhlberg, Blai Bonet and Hector Geffner.
  Learning Generalized Policies without Supervision Using GNNs.
  In Proceedings of the Nineteenth International Conference on Principles of Knowledge Representation and Reasoning (KR 2022), pp. 474–483. 2022.
  paper slides code citation

• Simon Ståhlberg, Blai Bonet and Hector Geffner.
  Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limits.
  In Proceedings of the Thirty-Second International Conference on Automated Planning and Scheduling (ICAPS 2022), pp. 629–637. 2022.
  paper slides citation

2021

• Simon Ståhlberg, Guillem Francès and Jendrik Seipp.
  Learning Generalized Unsolvability Heuristics for Classical Planning.
  In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI 2021), pp. 4175–4181. 2021.
  paper slides poster code citation

2017

• Simon Ståhlberg.
  Methods for Detecting Unsolvable Planning Instances using Variable Projection.
  PhD thesis, Linköping University, 2017.
  citation

• Simon Ståhlberg.
  Tailoring Pattern Databases for Unsolvable Planning Instances.
  In Proceedings of the Twenty-Seventh International Conference on Automated Planning and Scheduling (ICAPS 2017), pp. 274–282. 2017.
  paper citation

2016

• Meysam Aghighi, Christer Bäckström, Peter Jonsson and Simon Ståhlberg.
  Analysing Approximability and Heuristics in Planning Using the Exponential-Time Hypothesis.
  In Proceedings of the 22nd European Conference on Artificial Intelligence (ECAI 2016). 2016.
  citation

• Meysam Aghighi, Christer Bäckström, Peter Jonsson and Simon Ståhlberg.
  Refining complexity analyses in planning by exploiting the exponential time hypothesis.
  Annals of Mathematics and Artificial Intelligence 78, pp. 157–175. 2016.
  citation

2015

• Meysam Aghighi, Peter Jonsson and Simon Ståhlberg.
  Tractable Cost-Optimal Planning Over Restricted Polytree Causal Graphs.
  In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2015), pp. 3225–3231. 2015.
  paper citation

2013

• Christer Bäckström, Peter Jonsson and Simon Ståhlberg.
  Fast Detection of Unsolvable Planning Instances Using Local Consistency.
  In Proceedings of the Sixth Annual Symposium on Combinatorial Search (SoCS 2013), pp. 29–37. 2013.
  paper citation