Abstract
One of the most successful methods for planning in large partially observable stochastic domains is depth-limited forward search from the current belief state together with a utility estimation. However, when the environment is continuous and the number of possible actions is practically infinite, then abstractions have to be made before any forward search planning can be performed. The paper presents a method to dynamically generate such planning problem abstractions for a domain that is inspired by our research with unmanned aerial vehicles (UAVs). The planning problems are created by first stating the selection of points to fly to as an optimization problem. When the points have been selected, a set of possible paths between them are then created with a pathplanner and then forward search in the belief state space is applied. The method has been implemented and tested in simulation and the experiments show the importance of modelling both the dynamics of the environment and the limited computational resources of the architecture when searching for suitable parameters in the planning problem formulation procedure.
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