Planning with continuous resources in stochastic domains

Mausam, Emmanuel Benazera, Ronen Brafman, Nicolas Meuleau, Eric A. Hansen

Research output: Contribution to journalConference articlepeer-review

21 Scopus citations

Abstract

We consider the problem of optimal planning in stochastic domains with resource constraints, where resources are continuous and the choice of action at each step may depend on the current resource level. Our principal contribution is the HAO*algorithm, a generalization of the AO*algorithm that performs search in a hybrid state space that is modeled using both discrete and continuous state variables. The search algorithm leverages knowledge of the starting state to focus computational effort on the relevant parts of the state space. We claim that this approach is especially effective when resource limitations contribute to reachability constraints. Experimental results show its effectiveness in the domain that motivates our research - automated planning for planetary exploration rovers.

Original languageEnglish
Pages (from-to)1244-1251
Number of pages8
JournalIJCAI International Joint Conference on Artificial Intelligence
StatePublished - 1 Dec 2005
Externally publishedYes
Event19th International Joint Conference on Artificial Intelligence, IJCAI 2005 - Edinburgh, United Kingdom
Duration: 30 Jul 20055 Aug 2005

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