TitleAcquiring user models to test automated assistants
Publication TypeConference Paper
Year of Publication2013
AuthorsPickett, M, Aha, DW, Trafton, JG
Conference NameFlorida Artificial Intelligence Research Society Conference
PublisherAAAI Press
Conference LocationSt. Pete Beach, FL
Keywordsbehavioral models, imitation learning, machine learning

A central problem in decision support tasks is operator overload,
in which a human operator’s performance suffers because
he or she is overwhelmed by the cognitive requirements
of a task. To alleviate this problem, it would be useful to
provide the human operator with an automated assistant to
share some of the task’s cognitive load. However, the development
cycle for building an automated assistant is hampered
by the testing phase because this involves human user studies,
which are costly and time-consuming to conduct. As an
alternative to user studies, we propose acquiring user models,
which can be used as a proxy for human users during middle
iterations, thereby significantly shortening the development
cycle for rapid development. The primary contribution of this
paper is a method for coarsely testing automated assistants
by using user models acquired from traces gathered from various
individual human operators. We apply this method in a
case study in which we evaluate an automated assistant for
users operating in a simulation of multiple unmanned aerial

Refereed DesignationRefereed
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machine learning
imitation learning
behavioral models