Planning with continuous resources in stochastic domains

  • Authors:

  • Mausam;Emmanuel Benazera;Ronen Brafman;Nicolas Meuleau;Eric A. Hansen

  • Affiliations:

  • Dept. of Computer Science and Engineering, University of Washington, Seattle, WA;Research Institute for Advanced Computer Science and NASA Ames Research Center, Moffet Field, CA;QSS Group Inc., and NASA Ames Research Center, Moffet Field, CA;QSS Group Inc., and NASA Ames Research Center, Moffet Field, CA;Dept. of Computer Science and Engineering, Mississippi State University, MS

  • Venue:
  • IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

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.