Comparing Learning Techniques for Hidden Markov Models of Human Supervisory Control Behavior
Comparing Learning Techniques for Hidden Markov Models of Human Supervisory Control Behavior
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DOI：
10.2514/6.2009-1842
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发表时间：
2009-04
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影响因子：
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通讯作者：
Yves Boussemart;Jonathan C. Las Fargeas;M. Cummings;N. Roy
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文献类型：
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作者：
Yves Boussemart;Jonathan C. Las Fargeas;M. Cummings;N. Roy
人类行为的模型已经使用许多不同的框架建立。在本文中，我们利用隐马尔可夫模型（HALGORY）适用于人类的监督控制行为。更具体地说，我们模拟多个异构无人驾驶车辆系统的操作员的行为。HMM框架允许从可观察到的操作员与计算机接口的交互中推断出更高的操作员认知状态。例如，操作员动作的序列可以用于计算可能的操作员状态的概率分布。这样的模型能够检测与由模型学习的预期操作员行为的偏差。参数推理模型（如Hyndrome）的困难在于，大量参数必须手动指定或从示例数据中学习。我们比较了两种不同的监督学习技术和无监督HMM训练技术获得的行为模型。结果表明，最好的模型的人的监督控制行为是通过无监督学习。命名法
Models of human behaviors have been built using many different frameworks. In this paper, we make use of Hidden Markov Models (HMMs) applied to human supervisory control behaviors. More specifically, we model the behavior of an operator of multiple heterogeneous unmanned vehicle systems. The HMM framework allows the inference of higher operator cognitive states from observable operator interaction with a computer interface. For example, a sequence of operator actions can be used to compute a probability distribution of possible operator states. Such models are capable of detecting deviations from expected operator behavior as learned by the model. The difficulty with parametric inference models such as HMMs is that a large number of parameters must either be specified by hand or learned from example data. We compare the behavioral models obtained with two different supervised learning techniques and an unsupervised HMM training technique. The results suggest that the best models of human supervisory control behavior are obtained through unsupervised learning. Nomenclature