Marc Pickett

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Approved for public release; further dissemination unlimited. NOTICE: This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government, nor any agency thereof, nor any of their employees, nor any of their contractors, subcontractors, or their employees, make any warranty, express or(More)
We present The Cruncher, a simple representation framework and algorithm based on minimum description length for automatically forming an ontology of concepts from attribute-value data sets. Although unsupervised, when The Cruncher is applied to an animal data set, it produces a nearly zoologically accurate categorization. We demonstrate The Cruncher's(More)
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(More)
Many future decision support systems will be human-centric, i.e., require substantial human oversight and control. Because these systems often provide critical services, high assurance is needed that they satisfy their requirements. This paper, the product of an interdisciplinary research team of experts in formal methods, adaptive agents, and cognitive(More)
—Many future decision support systems will be human-centric, i.e., require substantial human oversight and control. Because these systems often provide critical services, high assurance will be needed that they satisfy their requirements. How to develop " high assurance human-centric decision systems " is unknown: while significant research has been(More)
We investigate the task of modeling open-domain, multi-turn, unstructured, multi-participant, conversational dialogue. We specifically study the effect of incorporating different elements of the conversation. Unlike previous efforts, which focused on mod-eling messages and responses, we extend the modeling to long context and participant's history. Our(More)