User modelling is a key element in successfully assisting intelligence analysts who must gather information and make decisions without being overloaded by the massive amounts of data available on a daily basis most of which are irrelevant. Furthermore, with user modelling, we can predict the goals and intentions of the analyst in order to better serve their information seeking tasks by providing better re-organization and presentation of data as well as pro-actively retrieve novel and relevant information as it arises. Our goal is to provide a dynamic user model of an analyst and work with him as he goes about his daily tasks.
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References
1.
BrownS. M.SantosE.Jr.BanksS. B.1999. Active user interfaces for building decision-theoretic systems. In Proceedings of the 1st Asia-Pacific Conference on Intelligent Agent Technology. Hong Kong. Pages 244–253.
2.
SurmanJ.HillmanR.SantosE.Jr.2003. Adversarial Inferencing for Generating Dynamic Adversary Behavior. In Proceedings of the SPIE 17th Annual International Symposium on Aerospace/Defense Sensing and Controls: AeroSense 2003, Orlando, FL.
3.
ClarkP.PorterB.1997. Building Concept Representations from Reusable Components. In Proceedings of AAAI 1997 Conference. Pages 369–376.
4.
DeckerK. S.SycaraK.1997. Intelligent Adaptive Information Agents. In Journal of Intelligent Information Systems. Vol 9. Pages 239–260.
5.
HwangC. H.1999. Incompletely and Imprecisely Speaking: Using dynamic ontology for representing and retrieving information. Infosleuth Group, Microeletronics and Computer Technology, 3500 West Balconnes Center Drive, Austin, TX 78759.
6.
NguyenH.SabaM. G.SantosE.Jr.BrownM. S.2000. Active user interface in a knowledge discovery and retrieval system. In Proceedings of the International Conference on Artificial Intelligence (ICAI2000). Las Vegas, Nevada. Page 339–344.
7.
SaltonG.McGillM.1983. Introduction to Modern Information RetrievalMcGraw-Hill Book Company.
8.
SantosE.Jr.ShimonyS. E.WilliamsE.Hybrid Algorithms for Approximate Belief Updating in Bayes Nets In International Journal of Approximate Reasoning, 17(2–3), 191–216, 1997.
9.
SantosE.Jr.BrownS. M.LejterM.NgaiG.BanksS.StytzM. R.1999. Dynamic User Model Construction with Bayesian Networks for Intelligent Information Queries. In Proceedings of the 12th International FLAIRS Conference. Orlando. FL. Page 3–7.
10.
SantosE.Jr.NguyenH.BrownS. M.2001. Kavanah: Active User Interface for Information Retrieval Application. In Proceedings of 2nd Asia-Pacific Conference on Intelligent Agent Technology. Page 412–423.
11.
SantosE.Jr.NguyenHZhaoQ.PukinskisE.2003. Empirical Evaluation of Adaptive User Modeling in a Medical Information Retrieval Application. In Proceedings of the Ninth User Modeling Conference. Johnstown. Pennsylvania. To appear.
12.
WidyantoroD. H.IoergerT. R.YenJ.1999. Adaptive agent for Learning changes in user Interests. In Proceedings of the International Conference on Information and Knowledge Management CIKM'99. Kansas City.
