Axel Vogler, Patrick Rammelt, Jörg Herbers, Dietmar Neumerkel
Understanding inference in probabilistic networks is a crucial point during the design phase. Their causal structure and locally defined parameters are intuitive to human experts. The global system induced by the local parameters can lead to results not intended by the human expert. Comprehending the behavior of dynamic probabilistic networks (DPN) for tuning the model is a time consuming task. Therefore this paper introduces tools supporting the design phase. The application of these tools is shown by means of a DPN for human driver modelling.
Keywords: Probabilistic Networks, Temporal Reasoning, User Modeling
Citation: Axel Vogler, Patrick Rammelt, Jörg Herbers, Dietmar Neumerkel: Visual Design Support in Dynamic Probabilistic Networks for Driver Modelling. In W.Horn (ed.): ECAI2000, Proceedings of the 14th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2000, pp.591-595.