Task-Oriented Active Sensing via Action Entropy Minimization.
Task-Oriented Active Sensing via Action Entropy Minimization.
复制标题
通过动作熵最小化的面向任务的主动感知。
DOI：
10.1109/access.2019.2941706
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Ҫavuşoğlu,MCenk
中科院分区：
文献类型：
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作者：
Greigarn,Tipakorn;Branicky,MichaelS;Ҫavuşoğlu,MCenk
在主动感知中，通常根据熵、条件熵、互信息等信息论度量来选择感知动作，使状态的不确定性最小化。对于以获取信息为目标的应用程序来说，这是合理的。然而，当有关状态的信息用于执行任务时，最小化状态不确定性可能不会导致提供对任务最有用的信息的感知操作。这是因为状态空间的某些子空间中的不确定性可能比其他子空间对任务的性能产生更大的影响，并且这种依赖性在任务的不同阶段可能会有所不同。将任务、不确定性和感知结合起来的一种方法是将问题建模为不确定条件下的顺序决策问题。不幸的是，这些问题的解决方案在计算上是昂贵的。本文提出了一种新的面向任务的主动感知方案，该方案在感知动作选择中考虑任务，选择使未来任务相关动作的不确定性最小化的感知动作，而不是选择状态的不确定性。仿真结果验证了该方法的有效性。
In active sensing, sensing actions are typically chosen to minimize the uncertainty of the state according to some information-theoretic measure such as entropy, conditional entropy, mutual information, etc. This is reasonable for applications where the goal is to obtain information. However, when the information about the state is used to perform a task, minimizing state uncertainty may not lead to sensing actions that provide the information that is most useful to the task. This is because the uncertainty in some subspace of the state space could have more impact on the performance of the task than others, and this dependence can vary at different stages of the task. One way to combine task, uncertainty, and sensing, is to model the problem as a sequential decision making problem under uncertainty. Unfortunately, the solutions to these problems are computationally expensive. This paper presents a new task-oriented active sensing scheme, where the task is taken into account in sensing action selection by choosing sensing actions that minimize the uncertainty in future task-related actions instead of state uncertainty. The proposed method is validated via simulations.