A Heuristic Search Approach to Planning with Continuous Resources in Stochastic Domains
This addresses planning problems for automated systems like rovers where resource limits are critical, but it is incremental as it builds on existing heuristic search methods.
The paper tackles optimal planning in stochastic domains with continuous resource constraints by introducing the HAO* algorithm, which generalizes AO* to handle hybrid state spaces and shows effectiveness in planetary rover exploration planning.
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.