A Look At Probabilistic Gaussian Process, Bayes Net, And Classifier Models For Prediction And Verification Of Human Supervisory Performance
This paper motivates why three classes of probabilistic models - Gaussian Process, Bayes Net, and Classifier - can be very useful in verifying collaborative interactions between humans and autonomy. These models have the ability to capture individual, average, and distributions of human capabilities (including supervisory tasking and communication) and human factors metrics such as working memory. We argue that these models can provide probabilistic information in a form that will enable a formal probabilistic approach to designing and fielding collaborative human-autonomous systems. In this paper, we will discuss key advantages and limitations of these models in the context of performance prediction, training/learning, and deployment. Our goal is to initiate a discussion of how data-driven models such as these can be incorporated into formal verification frameworks for human-machine systems. Copyright © 2014, Association for the Advancement of Artificial Intelligence. All rights reserved.
AAAI Spring Symposium - Technical Report
Number of Pages
Article; Proceedings Paper
Source API URL
Ahmed, Nisar; De Visser, Ewart; Shaw, Tyler; Parasuraman, Raja; and Mohammed-Ameen, Amira, "A Look At Probabilistic Gaussian Process, Bayes Net, And Classifier Models For Prediction And Verification Of Human Supervisory Performance" (2014). Scopus Export 2010-2014. 9275.