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New Optimization Functions for Potential Heuristics

Seipp, Jendrik and Pommerening, Florian and Helmert, Malte. (2015) New Optimization Functions for Potential Heuristics. In: Proceedings of the 25th International Conference on Automated Planning and Scheduling (ICAPS 2015). Palo Alto, California, pp. 193-201.

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Official URL: http://edoc.unibas.ch/42951/

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Abstract

Potential heuristics, recently introduced by Pommerening et al., characterize admissible and consistent heuristics for classical planning as a set of declarative constraints. Every feasible solution for these constraints defines an admissible heuristic, and we can obtain heuristics that optimize certain criteria such as informativeness by specifying suitable objective functions. The original paper only considered one such objective function: maximizing the heuristic value of the initial state. In this paper, we explore objectives that attempt to maximize heuristic estimates for all states (reachable and unreachable), maximize heuristic estimates for a sample of reachable states, maximize the number of detected dead ends, or minimize search effort. We also search for multiple heuristics with complementary strengths that can be combined to obtain even better heuristics.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Artificial Intelligence (Helmert)
UniBasel Contributors:Seipp, Jendrik and Helmert, Malte and Pommerening, Florian
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Publisher:AAAI Press
ISBN:978-1-57735-731-5
Note:Publication type according to Uni Basel Research Database: Conference paper
Language:English
Last Modified:16 Oct 2018 14:20
Deposited On:11 Oct 2017 07:31

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