A POMDP framework for human-in-the-loop system
A POMDP framework for human-in-the-loop system
复制标题
人在环系统的 POMDP 框架
DOI：
10.1109/cdc.2014.7040333
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
S. Sastry
中科院分区：
文献类型：
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作者：
C. Lam;S. Sastry
人类操作员参与了许多真实的世界系统，如汽车系统。传统的人工辅助功能，例如飞机上的警告系统和汽车上的自动制动系统，只是监控机器的状态，以防止人为错误并提高安全性。我们相信，下一代系统应该能够监控人和机器，并向它们提供适当的反馈。虽然在控制回路中有人有其优势，但它缺乏统一的建模框架来管理人与机器之间的反馈。在本文中，我们将介绍如何部分可观测马尔可夫决策过程（POMDP）可以作为一个统一的框架，在人在回路控制系统的三个主要组成部分-人的模型，机器的动态模型和观察模型。我们使用模拟来显示这个框架的好处。最后，我们概述了推进这一框架的关键挑战。
Human operators are involved in many real world systems such as automobile systems. Traditional human-assistance features such as warning systems in the aircraft and automatic braking systems in automobile only monitor the states of the machine in order to prevent human errors and enhance safety. We believe that next generation systems should be able to monitor both the human and the machine and give an appropriate feedback to them. Although having human in the control loop has its advantage, it lacks a unified modeling framework to manage the feedback between the human and the machine. In this paper, we will present how partially observable Markov decision process (POMDP) can be used as a unified framework for the three main components in a human-in-the-loop control system-the human model, the machine dynamic model and the observation model. We use simulations to show the benefits of this framework. Finally, we outline the key challenge to advance this framework.