Abstract
In real world applications planners are frequently faced with complex variable dependencies in high dimensional domains. In addition to that, they typically have to start from a very incomplete picture that is expanded only gradually as new information becomes available. In this contribution we deal with probabilistic graphical models, which have successfully been used for handling complex dependency structures and reasoning tasks in the presence of uncertainty. The paper discusses revision and updating operations in order to extend existing approaches in this field, where in most cases a restriction to conditioning and simple propagation algorithms can be observed. Furthermore, it is shown how all these operations can be applied to item planning and the prediction of parts demand in the automotive industry. The new theoretical results, modelling aspects, and their implementation within a software library were delivered by ISC Gebhardt and then involved in an innovative software system realized by Corporate IT for the world-wide item planning and parts demand prediction of the whole Volkswagen Group.
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Gebhardt, J., Kruse, R. (2005). Knowledge-Based Operations for Graphical Models in Planning. In: Godo, L. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2005. Lecture Notes in Computer Science(), vol 3571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11518655_2
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DOI: https://doi.org/10.1007/11518655_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27326-4
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