Scott M. Brown

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The United States creates or acquires increasingly more complex intelligence, surveillance, and reconnaissance (ISR) systems to maintain a strong, leading presence within the world. As a result, ISR systems have become more costly and difficult to manage. The research team focused on continuing previous year efforts of another team to utilize commercial(More)
The complexity of current computer systems and software warrants research into methods to decrease the cognitive load on users. Determining how to get the right information into the right form with the right tool at the right time has become a monumental task | one necessitating intelligent interface agents with the ability to predict the users' needs and(More)
An underlying problem of current interface agent research is the failure to adequately address eeective and eecient knowledge representations and associated methodologies suitable for modeling the users' interactions with the system. These user models lack the representational complexity to manage the uncertainty and dynamics involved in predicting user(More)
The complexity of" current soft.ware applications is overwhelming users. The need exists for inteUigent interface agents to address the problems of increasing taskload that is overwhelming the hunloax user. Interface agents could help alleviate user taskload by extracting and analyzing relevant information, and providing information abstractions of that(More)
This paper reports our preliminary design and implementation towards the development of Kavanah, a system to help users retrieve information and discover knowledge for a medical domain application. The goal of this system is to adaptively react to the dynamic changes in the user's interests and preferences in searching for information within the context of(More)
The proliferation of user modeling as a means of accurately capturing the beliefs, desires, and intent of users is apparent. We present a dynamic, uncertainty-based user model knowledge representation for use in an intelligent interface agent called GESIA. GESIA's numerical uncertainty management representation, which has its roots in Bayesian networks, not(More)
The PESKI (Probabilities, Expert Systems, Knowledge , and Inference) system attempts to address some of the problems in expert system design through the use of the Bayesian Knowledge Base (BKB) representation. Knowledge gathered from a domain expert is placed into this framework and inferencing under uncertainty is performed over it. However, by the nature(More)