Efficient Modeling of Temporally Variable User Properties with Dynamic Bayesian Networks

Boris Brandherm, Saarland University

Dynamic Bayesian networks (DBNs) are well suited to the modeling of temporally variable properties of computer users, such as time pressure and cognitive load. One challenge is to develop new methods for limiting the computational complexity of DBNs, so that they can be applied to real-time user modeling. A second goal is to design and test appropriate structures and preprocessing methods for DBNs that interpret data from a user’s motor behavior and speech, as well as physiological data.


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