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Paul Höft

PhD student

My general research interests are classical planning and machine learning.

Classical planning and machine learning

I focus on optimal classical planning and how to combine it with machine learning.

This includes learning admissible heuristics and advancing existing algorithms through online learning and/or dynamic algorithm configuration.

I completed my bachelor's degree in 2018 and my master's degree 2021 in computer science at the University of Basel and joined the RLPLAB in September 2021.



Paul Höft, David Speck, Jendrik Seipp (2023) Sensitivity Analysis for Saturated Post-hoc Optimization in Classical Planning Proceedings of the 26th European Conference on Artificial Intelligence (ECAI 2023), p. 1044-1051 Continue to DOI
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

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About the department