Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Big Data Analytics and Fuzzy Technology: Extracting Information from Social Data Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Decomposable Graphical Models on Learning, Fusion and Revision

verfasst von : Fabian Schmidt, Jörg Gebhardt, Rudolf Kruse

Erschienen in: Recent Developments and the New Direction in Soft-Computing Foundations and Applications

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Bilingual Students Benefit from Using Both Languages
Nächstes Kapitel Optimal Academic Ranking of Students in a Fuzzy Environment: A Case Study
Literatur
1.
R. Kruse et al., Computational Intelligence, A Methodological Introduction, 2nd edn. (Springer, London, 2016)MATH
2.
J. Whittaker, Graphical Models in Applied Multivariate Statistics (Wiley, Chichester, 1990)MATH
3.
J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Morgan Kaufmann, 1991)
4.
D. Koller, N. Friedman, Probabilistic Graphical Models: Principles and Techniques (MIT Press, Cambridge, Mass, 2009)MATH
5.
C. Borgelt, M. Steinbrecher, R. Kruse, Graphical Models: Representations for Learning, Reasoning and Data Mining, 2nd edn. (Wiley, Wiley Series in Computational Statistics, 2009)CrossRef
6.
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. 397–415CrossRef
7.
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. 51–68CrossRef
8.
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. 143–153CrossRef
9.
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
10.
L.E. Sucar, Relational Probabilistic Graphical Model. (Springer, London, 2015), pp. 219–235CrossRef
11.
L. Getoor, B. Taskar (eds.), Introduction to Statistical Relational Learning (MIT Press, Cambridge, MA, 2007)MATH
12.
P. Gardenfors, Knowledge in Flux: Modeling the Dynamics of Epistemic States (MIT Press, Cambridge, Mass, 1988)
13.
D. Gabbay, P. Smets (eds.), Handbook of Defeasable Reasoning and Uncertainty Management Systems: Belief Change, vol. 3 (Kluwer Academic Press, Dordrecht, Netherlands, 1998)
14.
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)MathSciNetCrossRef
15.
16.
H. Katsuno, A.O. Mendelzon, Propositional knowledge base revision and minimal change. Artif. Intell. 52(3), 263–294 (1991)MathSciNetCrossRef
17.
D. Gabbay, Controlled revision—an algorithmic approach for belief revision. J. Logic Comput. 13(1), 3–22 (2003)MathSciNetCrossRef
18.
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
19.
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)
20.
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
21.
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
22.
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
23.
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
24.
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. 153–165CrossRef
25.
J. Gebhardt, C. Borgelt, R. Kruse, H. Detmer, Knowledge revision in Markov networks. Math. Soft Comput. 11(2–3), 93–107 (2004)MathSciNetMATH
26.
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
27.
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
28.
K. Mu, W. Liu, Z. Jin, A general framework for measuring inconsistency through minimal inconsistent sets. Knowl. Inf. Syst. 27(1), 85–114 (2011)CrossRef
Metadaten
Titel
Decomposable Graphical Models on Learning, Fusion and Revision
verfasst von
Fabian Schmidt
Jörg Gebhardt
Rudolf Kruse
Copyright-Jahr
2018
Buch
Recent Developments and the New Direction in Soft-Computing Foundations and Applications

Print ISBN: 978-3-319-75407-9

Electronic ISBN: 978-3-319-75408-6

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-75408-6_34