Abstract
Graphical models are of high relevance for complex industrial applications. The Markov network approach is one of their most prominent representatives and an important tool to structure uncertain knowledge about high-dimensional domains in order to make reasoning in such domains feasible. Compared to conditioning the represented probability distribution on given evidence, the important belief change operation called revision has been almost entirely disregarded in the past, although it is of utmost relevance for real world applications. In this paper we focus on the problem of inconsistencies during revision in Markov networks. We formally introduce the revision operation and propose methods to specify, identify, and resolve inconsistencies. The revision and its inconsistency management has proven to be successful in a complex application for item planning and capacity management in the automotive industry at Volkswagen Group.
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Keywords
- Markov Network
- Probabilistic Graphical Model
- Initial Probability Distribution
- Planning Interval
- Iterative Revision
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Gebhardt, J., Klose, A., Wendler, J. (2013). Markov Network Revision: On the Handling of Inconsistencies. In: Moewes, C., Nürnberger, A. (eds) Computational Intelligence in Intelligent Data Analysis. Studies in Computational Intelligence, vol 445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32378-2_10
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DOI: https://doi.org/10.1007/978-3-642-32378-2_10
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