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 for world-wide planning realized by Corporate IT of Volkswagen Group.
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Gebhardt, J., Klose, A., Detmer, H., Rügheimer, F., Kruse, R. (2006). Graphical Models for Industrial Planning on Complex Domains. In: Della Riccia, G., Dubois, D., Kruse, R., Lenz, HJ. (eds) Decision Theory and Multi-Agent Planning. CISM International Centre for Mechanical Sciences, vol 482. Springer, Vienna. https://doi.org/10.1007/3-211-38167-8_8
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DOI: https://doi.org/10.1007/3-211-38167-8_8
Publisher Name: Springer, Vienna
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