Deep Learning for Cost-Optimal Planning: Task-Dependent Planner Selection

Sievers, Silvan and Katz, Michael and Sohrabi, Shirin and Samulowitz, Horst and Ferber, Patrick. (2019) Deep Learning for Cost-Optimal Planning: Task-Dependent Planner Selection. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI 2019), 33. pp. 7715-7723.

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Official URL: https://edoc.unibas.ch/71596/

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As classical planning is known to be computationally hard, no single planner is expected to work well across many planning domains. One solution to this problem is to use online portfolio planners that select a planner for a given task. These portfolios perform a classification task, a well-known and well-researched task in the field of machine learning. The classification is usually performed using a representation of planning tasks with a collection of hand-crafted statistical features. Recent techniques in machine learning that are based on automatic extraction of features have not been employed yet due to the lack of suitable representations of planning tasks. In this work, we alleviate this barrier. We suggest representing planning tasks by images, allowing to exploit arguably one of the most commonly used and best developed techniques in deep learning. We explore some of the questions that inevitably rise when applying such a technique, and present various ways of building practically useful online portfolio-based planners. An evidence of the usefulness of our proposed technique is a planner that won the cost-optimal track of the International Planning Competition 2018
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Artificial Intelligence (Helmert)
UniBasel Contributors:Sievers, Silvan and 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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Last Modified:01 Jun 2023 14:30
Deposited On:20 Aug 2019 11:58

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