A heuristic search approach to planning with continuous resources in stochastic domains

  • Authors:

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

  • Affiliations:

  • NASA Ames Research Center, Moffet Field, CA;LAAS-CNRS, Université de Toulouse, Toulouse Cedex 4, France;Department of Computer Science, Ben-Gurion University, Beer-Sheva, Israel;Department of Computer Science and Engineering, Mississippi State University, Mississippi State, MS;Department of Computer Science and Engineering, University of Washington, Seattle, WA

  • Venue:
  • Journal of Artificial Intelligence Research
  • Year:
  • 2009

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Abstract

We consider the problem of optimal planning in stochastic domains with resource constraints, where the resources are continuous and the choice of action at each step depends on resource availability. We introduce 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, where the continuous variables represent monotonic resources. Like other heuristic search algorithms, HAO* leverages knowledge of the start state and an admissible heuristic to focus computational effort on those parts of the state space that could be reached from the start state by following an optimal policy. We show that this approach is especially effective when resource constraints limit how much of the state space is reachable. Experimental results demonstrate its effectiveness in the domain that motivates our research: automated planning for planetary exploration rovers.