R&D

Knowledge and Data Fusion in Probabilistic Networks

Citation: Laskey, K.B. and T.S. Levitt, "Multisource Fusion for Probabilistic Detection and Opportunistic Assessment of Terrorist Threats".

Citation: Laskey, K. B. and S. M. Mahoney, "Knowledge and Data Fusion in Probabilistic Networks", submitted to Machine Learning Journal Special Issue on Fusion of Domain Knowledge with Data for Decision Support.

Abstract

Intelligent systems use internal representations to mediate the transformation from percepts to goal-directed actions. Intelligent learning agents use environmental feedback to modify their internal representations to improve performance over time and adapt to changing circumstances. All learning involves knowledge-data fusion to some degree. Bayesian learning, the focus of this paper, is specifically designed to incorporate both expert knowledge and observations. We use the term “data” to refer both to collections of cases and to statements about the domain provided by experts and knowledge engineers and used to construct internal representations. The term “knowledge” refers to the internal representation itself, which we take to be a collection of Bayesian network fragments. We describe a prequential learning agent architecture for bounded rational action and learning under uncertainty. We describe recent extensions to Bayesian networks that provide sufficient representation power for expressing general prequential learning agent models. We describe tools and techniques to support a process in which models are constructed and refined using a combination of inputs from experts and environmental feedback.

KEY WORDS: Bayesian Networks, Bayesian Learning, Graphical Probabilistic Models, Knowledge Elicitation, Object Oriented Bayesian Networks, Prequential Probability.

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