Adaptive Information Belief Space Planning

• URL: http://arxiv.org/abs/2201.05673v1
• Date: Fri, 14 Jan 2022 21:12:00 GMT
• Title: Adaptive Information Belief Space Planning
• Authors: Moran Barenboim and Vadim Indelman
• Abstract summary: We focus on making informed decisions efficiently, using reward functions that explicitly deal with uncertainty. We derive bounds on the expected information-theoretic reward function and, as a consequence, on the value function. We then propose a method to refine aggregation to achieve identical action selection with a fraction of the computational time.
• Score: 9.365993173260316
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reasoning about uncertainty is vital in many real-life autonomous systems. However, current state-of-the-art planning algorithms cannot either reason about uncertainty explicitly, or do so with a high computational burden. Here, we focus on making informed decisions efficiently, using reward functions that explicitly deal with uncertainty. We formulate an approximation, namely an abstract observation model, that uses an aggregation scheme to alleviate computational costs. We derive bounds on the expected information-theoretic reward function and, as a consequence, on the value function. We then propose a method to refine aggregation to achieve identical action selection with a fraction of the computational time.

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