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ECAI-2000 Conference Paper

Visual Design Support in Dynamic Probabilistic Networks for Driver Modelling

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.

ECAI-2000 is organised by the European Coordinating Committee for Artificial Intelligence (ECCAI) and hosted by the Humboldt University on behalf of Gesellschaft für Informatik.