Abstract
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
