Abstract
Industrial applications often face elaborated problems. In order to solve them properly a great deal of complexity and data diversity has to be managed. In this paper we present a planning system that is used globally by the Volkswagen Group. We introduce the specific challenges that this industrial application faces, namely a high complexity paired with diverse heterogeneous data sources, and describe how the problem has been modelled and solved. We further introduce the core technology we used, the revision of Markov networks. We further motivate the need to handle planning inconsistencies and present our framework consisting of six main components: Prevention, Detection, Analysis, Explanation, Manual Resolution, and Automatic Elimination.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
R. Kruse et al., Computational Intelligence, A Methodological Introduction, 2nd edn. (Springer, London, 2016)
J. Whittaker, Graphical Models in Applied Multivariate Statistics (Wiley, Chichester, 1990)
J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Morgan Kaufmann, 1991)
D. Koller, N. Friedman, Probabilistic Graphical Models: Principles and Techniques (MIT Press, Cambridge, Mass, 2009)
C. Borgelt, M. Steinbrecher, R. Kruse, Graphical Models: Representations for Learning, Reasoning and Data Mining, 2nd edn. (Wiley, Wiley Series in Computational Statistics, 2009)
J. Gebhardt, R. Kruse, Background to and perspectives of possibilistic graphical models, in Applications of Uncertainty Formalisms, ed. by A. Hunter, S. Parsons (Springer, Berlin, Heidelberg, 1998), pp. 397–415
C. Borgelt, J. Gebhardt, R. Kruse, Possibilistic graphical models, in Computational Intelligence in Data Mining, ISSEK’98 (Udine, Italy), ed. by G.D. Riccia, R. Kruse, H.-J. Lenz (Springer, Wien, 2000), pp. 51–68
J. Gebhardt, R. Kruse, Learning possibilistic networks from data, in Learning from Data, Artificial Intelligence and Statistics 5, ed. by D. Fisher, H. Lenz. Lecture Notes in Statistics, vol. 112 (Springer, New York, 1996), pp. 143–153
B. Amor, Possibilistic graphical models: from reasoning to decision making, in Fuzzy Logic and Applications: 10th International Workshop, WILF, 2013, Genoa, Italy, November 19–22, 2013. Proceedings, ed. by F. Masulli, G. Pasi, R. Yager (Springer International Publishing, Cham, 2013), pp. 86–99
L.E. Sucar, Relational Probabilistic Graphical Model. (Springer, London, 2015), pp. 219–235
L. Getoor, B. Taskar (eds.), Introduction to Statistical Relational Learning (MIT Press, Cambridge, MA, 2007)
P. Gardenfors, Knowledge in Flux: Modeling the Dynamics of Epistemic States (MIT Press, Cambridge, Mass, 1988)
D. Gabbay, P. Smets (eds.), Handbook of Defeasable Reasoning and Uncertainty Management Systems: Belief Change, vol. 3 (Kluwer Academic Press, Dordrecht, Netherlands, 1998)
C.E. Alchourron, P.G. Ardenfors, D. Makinson, On the logic of theory change: partial meet contraction and revision functions. J. Symbol. Logic 50(02), 510–530 (1985)
A. Darwiche, On the logic of iterated belief revision. Artif. Intell. 89(1–2), 1–29 (1997)
H. Katsuno, A.O. Mendelzon, Propositional knowledge base revision and minimal change. Artif. Intell. 52(3), 263–294 (1991)
D. Gabbay, Controlled revision—an algorithmic approach for belief revision. J. Logic Comput. 13(1), 3–22 (2003)
B. Nebel, Base revision operations and schemes: representation, semantics and complexity, in Proceedings of the Eleventh European Conference on Artificial Intelligence (ECAI94) (Wiley, Amsterdam, The Netherlands, 1994), pp. 341–345
J. Gebhardt, H. Detmer, A.L. Madsen, Predicting parts demand in the automotive industry—an application of probabilistic graphical models, in Proceedings 19th International Joint Conference on Uncertainty in Artificial Intelligence (Acapulco, 2003)
J. Gebhardt, A. Klose, H. Detmer, F. Ruegheimer, R. Kruse, Graphical models for industrial planning on complex domains, in Decision Theory and Multi-Agent Planning, ser. CISM Courses and Lectures, ed. by G. Della, D. Riccia, Dubois, R. Kruse, H.-J. Lenz, vol. 482 (Springer, 2006), pp. 131–143
Y.W. Teh, M. Welling, On improving the efficiency of the iterative proportional fitting procedure, in Proceedings of the 9th international Workshop on Artificial Intelligence and Statistics in Key West, Florida, ed. by C.M. Bishop, B.J. Frey (2003), pp. 1–8
F. Pukelsheim, B. Simeone, On the iterative proportional fitting procedure: structure of accumulation points and L1-error analysis, in Structure, vol. 05 (2009), p. 28
F. Schmidt, J. Wendler, J. Gebhardt, R. Kruse, Handling inconsistencies in the revision of probability distributions, in Hybrid Artificial Intelligent Systems: 8th International Conference, HAIS 2013, Salamanca, Spain, September 11–13, 2013. Proceedings, ed. by J.-S. Pan, M.M. Polycarpou, M. Wozniak, L.F. de Carvalho, H. Quinti´an, E. Corchado (Springer, Berlin, Heidelberg, 2013), pp. 598–607
J. Gebhardt, A. Klose, J. Wendler, Markov network revision: on the handling of inconsistencies, in Computational Intelligence in Intelligent Data Analysis, ser. Studies in Computational Intelligence, ed. by C. Moewes, A. Nurnberger, vol. 445 (Springer, Berlin, Heidelberg, 2012), pp. 153–165
J. Gebhardt, C. Borgelt, R. Kruse, H. Detmer, Knowledge revision in Markov networks. Math. Soft Comput. 11(2–3), 93–107 (2004)
F. Schmidt, J. Gebhardt, R. Kruse, Handling revision inconsistencies: creating useful explanations, in HICSS-48, Proceedings, 5–8 January 2015, Koloa, Kauai, HI, USA, ed. by T.X. Bui, R.H. Sprague (IEEE Computer Society, 2015), pp. 1–8
A. Hunter, S. Konieczny et al., Measuring inconsistency through minimal inconsistent sets, in Proceedings of the Eleventh International Conference on Principles of Knowledge Representation and Reasoning (Sydney, Australia, 2008), pp. 358–366
K. Mu, W. Liu, Z. Jin, A general framework for measuring inconsistency through minimal inconsistent sets. Knowl. Inf. Syst. 27(1), 85–114 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Schmidt, F., Gebhardt, J., Kruse, R. (2018). Decomposable Graphical Models on Learning, Fusion and Revision. In: Zadeh, L., Yager, R., Shahbazova, S., Reformat, M., Kreinovich, V. (eds) Recent Developments and the New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol 361. Springer, Cham. https://doi.org/10.1007/978-3-319-75408-6_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-75408-6_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-75407-9
Online ISBN: 978-3-319-75408-6
eBook Packages: EngineeringEngineering (R0)