@inproceedings{Deventer00:CDS,
    type = {inproceedings},
    key = {Deventer00:CDS},
    title = {Control of Dynamic Systems Using Bayesian Networks},
    author = {Rainer Deventer and Joachim Denzler and Heinrich Niemann},
    booktitle = {IBERAMIA/SBIA Workshops (Atibaia, S\~ao Paulo, Brazil)},
    year = {2000},
    address = {S\~ao Paulo},
    editor = {Leliane Nunes de Barros et al.},
    month = {November},
    pages = {33-39},
    publisher = {Tec Art Editora},
    abstract = {Bayesian networks for the static as well as for the dynamic case have gained an enormous interest in the research community of artificial intelligence, machine learning and pattern recognition. Although the parallels between dynamic Bayesian networks and Kalman filters are well known since many years, Bayesian networks have not been applied to problems in the area of adaptive control of dynamic systems. In our work we exploit the well know similarities between Bayesian networks and Kalman filters to model and control linear dynamic systems using dynamic Bayesian networks. We show, how the model is used to calculate appropriate input signals for the dynamic system to achieve a required output signal. First the desired output value is entered as additional evidence. Then marginalization results in the most likely values of the input nodes. The experiments show that with our approach the desired value is reached in reasonable time and with great accuracy. Additionally, oscillating systems can be handled. The benefits of the proposed approach are the model based control strategy and the possibility to learn the structure and probabilities of the Bayesian network from examples.},
    keywords = {Bild},
}