Software safety: why, what, and how
ACM Computing Surveys (CSUR)
Telerobotics, automation, and human supervisory control
Telerobotics, automation, and human supervisory control
The precis of Project Ernestine or an overview of a validation of GOMS
CHI '92 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Modeling and prediction of human behavior
Neural Computation
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Honest Signals: How They Shape Our World
Honest Signals: How They Shape Our World
Multiple Heterogeneous Unmanned Aerial Vehicles
Multiple Heterogeneous Unmanned Aerial Vehicles
Toward predicting the performance of novice CAD users based on their profiled technical attributes
Engineering Applications of Artificial Intelligence
Genetic programming based blind image deconvolution for surveillancesystems
Engineering Applications of Artificial Intelligence
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Behavioral models of human operators engaged in complex, time-critical high-risk domains, such as those typical in Human Supervisory Control (HSC) settings, are of great value because of the high cost of operator failure. We propose that Hidden Semi-Markov Models (HSMMs) can be employed to model behaviors of operators in HSC settings where there is some intermittent human interaction with a system via a set of external controls. While regular Hidden Markov Models (HMMs) can be used to model operator behavior, HSMMs are particularly suited to time-critical supervisory control domains due to their explicit representation of state duration. Using HSMMs, we demonstrate in an unmanned vehicle supervisory control environment that such models can accurately predict future operator behavior both in terms of states and durations.