Adaptive Information Belief Space Planning

Adaptive Information Belief Space Planning

Moran Barenboim, Vadim Indelman

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Main Track. Pages 4588-4596. https://doi.org/10.24963/ijcai.2022/637

Reasoning about uncertainty is vital in many real-life autonomous systems. However, current state-of-the-art planning algorithms either cannot reason about uncertainty explicitly, or do so with 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 in a fraction of the computational time.
Keywords:
Planning and Scheduling: Planning under Uncertainty
Planning and Scheduling: Planning Algorithms
Planning and Scheduling: Planning with Incomplete Information
Planning and Scheduling: POMDPs
Planning and Scheduling: Robot Planning