My research interests are in the field of artificial intelligence, specifically in the area of classical planning. I am currently investigating the use of neural networks to learn generalized policies, in order to solve a wide range of planning problems. My focus is on how deep learning can be used to improve traditional planning systems, which are not able to learn from experience or examples, and thus do not scale well to large problems.
Simon Ståhlberg
Principal Research Engineer
Researcher within artificial intelligence with a focus on combining automated planning and deep learning.
Artificial intelligence, in the area of classical planning
I am a researcher within artificial intelligence with a focus on combining automated planning and deep learning.
CV in brief
Education
I pursued my education at Linköping University, where I completed my Bachelor's and Master's degrees in Computer Science in 2010 and 2012, respectively, and I received my PhD in 2017.
Employment
After completing my studies, I worked in the industry for a while before returning to research as a postdoc when this lab was founded.
Awards
My work in this lab has been recognized by several notable awards in the field of artificial intelligence, including:
- ICAPS 2022 Best Paper Award at the 32nd International Conference on Automated Planning and Scheduling for the paper "Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limits"
- IJCAI 2021 Distinguished Paper Award at the 30th International Joint Conference on Artificial Intelligence for the paper "Learning Generalized Unsolvability Heuristics for Classical Planning"
Publications
2023
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2022
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