论文标题
自适应信息信念空间规划
Adaptive Information Belief Space Planning
论文作者
论文摘要
关于不确定性的推理在许多现实生活自主系统中至关重要。但是，当前的最新计划算法不能明确地出现不确定性的原因，也不能承担较高的计算负担。在这里，我们专注于使用明确处理不确定性的奖励功能有效地做出明智的决策。我们制定了一个近似值，即一种抽象观察模型，该模型使用聚合方案来减轻计算成本。我们在预期的信息理论奖励函数上得出界限，并因此在价值函数上得出了界限。然后，我们提出了一种方法来完善聚合以在计算时间的一小部分中实现相同的动作选择。
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.
