Delfi: Online Planner Selection for Cost-Optimal Planning (planner abstract)

Katz, Michael and Sohrabi, Shirin and Samulowitz, Horst and Sievers, Silvan. (2018) Delfi: Online Planner Selection for Cost-Optimal Planning (planner abstract). Ninth International Planning Competition (IPC 2018).

Full text not available from this repository.

Official URL: https://edoc.unibas.ch/68708/

Downloads: Statistics Overview


Cost-optimal planning has not seen many successful approaches that work well across all domains. Some costoptimal planners excel on some domains, while exhibiting less exciting performance on others. For a particular domain, however, there is often a cost-optimal planner that works extremely well. For that reason, portfolio-based techniques have recently become popular. These either decide offline on a particular resource allocation scheme for a given collection of planners or try to perform an online classification of a given planning task to select a planner to be applied to solving the task at hand. Our planner Delfi is an online portfolio planner. In contrast to existing techniques, Delfi exploits deep learning techniques to learn a model that predicts which of the planners in the portfolio can solve a given planning task within the imposed time and memory bounds. Delfi uses graphical representations of a planning task which allows exploiting existing tools for image convolution. In this planner abstract, we describe the techniques used to create our portfolio planner.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Artificial Intelligence (Helmert)
UniBasel Contributors:Sievers, Silvan
Item Type:Other
Number of Pages:12
Note:Publication type according to Uni Basel Research Database: Other publications
Related URLs:
Last Modified:28 Oct 2020 13:55
Deposited On:28 Oct 2020 13:55

Repository Staff Only: item control page