Simplified Planner Selection

Ferber, Patrick. (2020) Simplified Planner Selection. Proceedings of the 12th Workshop on Heuristics and Search for Domain-independent Planning (HSDIP). pp. 102-110.

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Official URL: https://edoc.unibas.ch/81223/

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There exists no planning algorithm that outperforms all oth- ers. Therefore, it is important to know which algorithm works well on a task. A recently published approach uses either im- age or graph convolutional neural networks to solve this prob- lem and achieves top performance. Especially the transforma- tion from the task to an image ignores a lot of information. Thus, we would like to know what the network is learning and if this is reasonable. As this is currently not possible, we take one step back. We identify a small set of simple graph features and show that elementary and interpretable machine learning techniques can use those features to outperform the neural network based approach. Furthermore, we evaluate the importance of those features and verify that the performance of our approach is robust to changes in the training and test data.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Artificial Intelligence (Helmert)
UniBasel Contributors:Ferber, Patrick
Item Type:Other
Note:Publication type according to Uni Basel Research Database: Other publications
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Last Modified:23 Feb 2021 12:24
Deposited On:23 Feb 2021 12:24

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