Foundations of Artificial Intelligence (FAI) Group

Maximilian Fickert

Hi! I'm a PhD student at the chair of Prof. Jörg Hoffmann in the Computer Science department at Saarland University. My research focuses on adaptive search techniques in heuristic search and planning, e.g., online refinement of partial delete relaxation heuristics, exploiting uncertainty in deterministic planning, real-time search algorithms, and online replanning.

Dissertation

• Adaptive Search Techniques in AI Planning and Heuristic Search, Saarland University, 2022. (PDF at SciDok)




Conference Papers

• M. Fickert, T. Gu, and W. Ruml, New Results in Bounded-Suboptimal Search, Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI'22), 2022. (PDF)

• M. Fickert, T. Gu, and W. Ruml, Bounded-Cost Search Using Estimates of Uncertainty, Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI'21), 2021. (PDF)

• M. Fickert, I. Gavran, I. Fedotov, J. Hoffmann, R. Majumdar, and W. Ruml, Choosing the Initial State for Online Replanning, Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI'21), 2021. (PDF)

• M. Fickert, A Novel Lookahead Strategy for Delete Relaxation Heuristics in Greedy Best-First Search, Proceedings of the 30th International Conference on Automated Planning and Scheduling (ICAPS'20), Nancy, France, 2020. (PDF)

• M. Fickert, T. Gu, L. Staut, W. Ruml, J. Hoffmann, and M. Petrik, Beliefs We Can Believe In: Replacing Assumptions with Data in Real-Time Search, Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'20), New York City, USA, 2020. (PDF)

• J. Groß, A. Torralba, and M. Fickert, Novel Is Not Always Better: On the Relation Between Novelty and Dominance Pruning, Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'20), New York City, USA, 2020. (PDF)

• R. Eifler, M. Fickert, J. Hoffmann, and W. Ruml, Refining Abstraction Heuristics During Real-Time Planning, Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, USA, 2019. (PDF)

• M. Fickert, Making Hill-Climbing Great Again through Online Relaxation Refinement and Novelty Pruning, Proceedings of the 11th Annual Symposium on Combinatorial Search (SOCS'18), Stockholm, Sweden, 2018. (PDF)

• R. Eifler and M. Fickert, Online Refinement of Cartesian Abstraction Heuristics, Proceedings of the 11th Annual Symposium on Combinatorial Search (SOCS'18), Stockholm, Sweden, 2018. (PDF)

• M. Fickert, D. Gnad, and J. Hoffmann, Unchaining the Power of Partial Delete Relaxation, Part II: Finding Plans with Red-Black State Space Search, Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018. (PDF) (Technical Report)

• M. Fickert and J. Hoffmann, Ranking Conjunctions for Partial Delete Relaxation Heuristics in Planning, Proceedings of the 10th Annual Symposium on Combinatorial Search (SOCS'17), Pittsburgh, PA, USA, 2017. (PDF)

• M. Fickert and J. Hoffmann, Complete Local Search: Boosting Hill-Climbing through Online Relaxation Refinement, Proceedings of the 27th International Conference on Automated Planning and Scheduling (ICAPS'17), Pittsburgh, PA, USA, 2017. (PDF)

• R. Becker, M. Fickert, and A. Karrenbauer, A Novel Dual Ascent Algorithm for Solving the Min-Cost Flow Problem, Proceedings of the Eighteenth Workshop on Algorithm Engineering and Experiments (ALENEX), 2016. (PDF)

• J. Hoffmann and M. Fickert, Explicit Conjunctions w/o Compilation: Computing hFF(PiC) in Polynomial Time, Proceedings of the 25th International Conference on Automated Planning and Scheduling (ICAPS'15), Jerusalem, Israel, 2015. (PDF)




Journal Articles

• M. Fickert and J. Hoffmann, Online Relaxation Refinement for Satisficing Planning: On Partial Delete Relaxation, Complete Hill-Climbing, and Novelty Pruning, Journal of Artificial Intelligence Research, Volume 73, 2022, pages 67-115. (PDF at JAIR)

• M. Fickert, J. Hoffmann, and M. Steinmetz, Combining the Delete Relaxation with Critical-Path Heuristics: A Direct Characterization, Journal of Artificial Intelligence Research, Volume 56, 2016, pages 269-327. (PDF at JAIR)




Workshops and Others

• M. Fickert, P. Lauer, Beating LM-cut with LM-cut: Quick Cutting and Practical Tie Breaking for the Precondition Choice Function, Proceedings of the 12th Workshop on Heuristic Search for Domain-Independent Planning (HSDIP'20), at ICAPS'20. (PDF)

• M. Fickert, T. Gu, L. Staut, S. Lekyang, W. Ruml, J. Hoffmann, M. Petrik, Real-time Planning as Data-driven Decision-making, Proceedings of the Workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL), at ICAPS'20.

• I. Gavran, M. Fickert, I. Fedotov, J. Hoffmann, and R. Majumdar, Simultaneous Re-Planning and Plan Execution for Online Job Arrival, Proceedings of the 11th Workshop on Heuristic Search for Domain-Independent Planning (HSDIP'19), at ICAPS'19. (PDF)

• M. Fickert, D. Gnad, P. Speicher, and J. Hoffmann, SaarPlan: Combining Saarland's Greatest Planning Techniques, Proceedings of the 9th International Planning Competition (IPC 2018), at ICAPS'18. Runner-up in the Agile and Cost-Bounded tracks. (PDF)

• M. Fickert, J. Hoffmann, OLCFF: Online-Learning hCFF, Proceedings of the 9th International Planning Competition (IPC 2018), at ICAPS'18. (PDF)

• R. Eifler and M. Fickert, Online Refinement of Cartesian Abstraction Heuristics, Proceedings of the 10th Workshop on Heuristic Search for Domain-Independent Planning (HSDIP'18), at ICAPS'18. (PDF)



The source code of the implementations of algorithms described in my papers is available on my Github page. See also the Software page of our group.



I am/have been involved as a teaching assistant in the following courses:

• Programmierung II (2018–2021)
• Artificial Intelligence (2017)
• Automatic Planning (2016, 2019)




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