Explainable Planner Selection for Classical Planning

Ferber, Patrick and Seipp, Jendrik. (2022) Explainable Planner Selection for Classical Planning. In: The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22). pp. 9741-9749.

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Official URL: https://edoc.unibas.ch/93505/

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Since no classical planner consistently outperforms all others, it is important to select a planner that works well for a given classical planning task. The two strongest approaches for planner selection use image and graph convolutional neural networks. They have the drawback that the learned models are complicated and uninterpretable. To obtain explainable models, we identify a small set of simple task features and show that elementary and interpretable machine learning techniques can use these features to solve roughly as many tasks as the complex approaches based on neural networks
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Artificial Intelligence (Helmert)
UniBasel Contributors:Ferber, Patrick
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Publisher:AAAI Press
Note:Publication type according to Uni Basel Research Database: Conference paper
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edoc DOI:
Last Modified:14 Mar 2023 14:26
Deposited On:15 Feb 2023 11:12

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