Fast Action Elimination for Efficient Decision Making and Belief Space Planning Using Bounded Approximations

In this paper we develop a novel paradigm to efficiently solve decision making and planning problems, and demonstrate it for the challenging case of planning under uncertainty. While conventional methods tend to optimize properties of specific problems, and sacrifice performance in order to reduce their complexity, our approach has no coupling to a specific problem, and relies solely on the structure of the general decision problem, in order to directly reduce its computational cost, with no influence over the quality of solution, nor the maintained state. Using bounded approximations of the state, we can easily eliminate unfit actions, while sparing the need to exactly evaluate the objective function for all the candidate actions. The original problem can then be solved considering a minimal subset of candidates. Since the approach is especially relevant when the action domain is large, and the objective function is expensive to evaluate, we later extend the discussion specifically for decision making under uncertainty and belief space planning, and present dedicated and practical tools, in order to apply the method to a sensor deployment problem. This paper continues our previous work towards efficient decision making.