Assessing temporally variable user properties with dynamic Bayesian networks

Autor: Schäfer, Ralph and Weyrath, Thomas

Herausgeber:

Bayesian networks have been successfully applied to the assessment of user properties which remain unchanged during a session. However, many properties of a person vary over time, thus raising new questions of network modeling. In this paper we characterize different types of dependencies that occur in networks that deal with the modeling of temporally variable user properties. We show how existing techniques of applying dynamic probabilistic networks can be adapted for the task of modeling the dependencies in dynamic Bayesian networks. We illustrate the proposed techniques using examples of emergency calls to the fire department of the city of Saarbrücken. The fire department officers are experienced in dealing with emergency calls from callers whose available working memory capacity is temporarily limited. We develop a model which reconstructs the officers' assessments of a caller's working memory capacity.