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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
Bibliography
C. Borgelt and R. Kruse. Graphical Models—Methods for Data Analysis and Mining. J. Wiley & Sons, Chichester, 2002.
W.L. Buntine. Operations for learning with graphical models. Journal of Artificial Intelligence Research, 2:159–225, 1994.
E. Castillo, J.M. Guitérrez, and A.S. Hadi. Expert Systems and Probabilistic Network Models. Springer-Verlag, New York, 1997.
D.M. Chickering, D. Geiger, and D. Heckerman. Learning Bayesian networks from data. Machine Learning, 20(3): 197–243, 1995.
G.F. Cooper and E. Herskovits. A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9:309–347, 1992.
R.G. Cowell, A.P. Dawid, S.L. Lauritzen, and D.J. Spiegelhalter. Probabilistic Networks and Expert Systems. Springer-Verlag, New York, 1999.
N. Friedman. The Bayesian structural EM algorithm. In Proc. of the 14th Conference on Uncertainty in AI, pages 129–138, 1998.
P. Gärdenfors. Knowledge in the Flux—Modeling the Dynamics of Epistemic States. MIT press, Cambridge, MA, 1988.
J. Gebhardt. The revision operator and the treatment of inconsistent stipulations of item rates. Project EPL: Internal Report 9. ISC Gebhardt and Volkswagen Group, K-DOB-11, 2001.
J. Gebhardt. Knowledge revision in markov networks, to appear in Mathware and Soft Computing, 2004.
J. Gebhardt, H. Detmer, and A.L. Madsen. Predicting parts demand in the automotive industry — an application of probabilistic graphical models. In Proc. Int. Joint Conf. on Uncertainty in Artificial Intelligence (UAI’03, Acapulco, Mexico), Bayesian Modelling Applications Workshop, 2003.
D. Geiger, T.S. Verma, and J. Pearl. Identifying independence in Bayesian networks. Networks, 20:507–534, 1990.
J.M. Hammersley and P.E. Clifford. Markov fields on finite graphs and lattices. Cited in Isham (1981), 1971.
V. Isham. An introduction to spatial point processes and markov random fields. Int. Statistical Review, 49:21–43, 1981.
F. Khalfallah and K. Mellouli. Optimized algorithm for learning Bayesian networks from data. In Proc. 5th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQUARU’99), pages 221–232, 1999.
S. L. Lauritzen and D. J. Spiegelhalter. Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society, Series B, 2(50): 157–224, 1988.
S.L. Lauritzen. Graphical Models. Oxford University Press, 1996.
J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufman, San Mateo, USA, 1988. (2nd edition 1992).
M. Singh and M. Valtorta. Construction of Bayesian network structures from data: Brief survey and efficient algorithm. Int. Journal of Approximate Reasoning, 12:111–131, 1995.
P. Sprites and C. Glymour. An algorithm for fast recovery of sparse causal graphs. Social Science Computing Review, 9(1):62–72, 1991.
H. Steck. On the use of skeletons when learning Bayesian networks. In Proc. of the 16th Conference on Uncertainty in AI, pages 558–565, 2000.
T. Verma and J. Pearl. An algorithm for deciding whether a set of observed independencies has a causal explanation. In Proc. 8th Conference on Uncertainty in AI, pages 323–330, 1992.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 CISM, Udine
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/3-211-38167-8_8
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-31787-7
Online ISBN: 978-3-211-38167-0
eBook Packages: EngineeringEngineering (R0)