A Heuristic Search Approach to Planning with Continuous Resources in
  Stochastic Domains
  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.
