Abstract
The handling of uncertain information is of crucial importance for the success of expert systems. This paper gives an overview on logic-based approaches to probabilistic reasoning and goes into more details about recent developments for relational, respectively first-order, probabilistic methods like Markov logic networks, and Bayesian logic programs. In particular, we will feature the maximum entropy approach as a powerful and elegant method that combines convenience with respect to knowledge representation with excellent inference properties. We briefly describe some systems for probabilistic reasoning, and go into more details on the KReator system as a versatile toolbox for probabilistic relational learning, modelling, and inference. Moreover, we will illustrate applications of probabilistic logics in various scenarios.
The research reported here was partially supported by the Deutsche Forschungsgemeinschaft (grants KE 1413/2-2 and BE 1700/7-2).
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
Preview
Unable to display preview. Download preview PDF.
References
Baumbach, J., Bunkowski, A., Lange, S., Oberwahrenbrock, T., Kleinbölting, N., Rahmen, S., Baumbach, J.I.: IMS2 – An integrated medical software system for early lung cancer detection using ion mobility spectometry data of human breath. J. of Integrative Bioinformatics 4(3) (2007)
Baumbach, J.I., Westhoff, M.: Ion mobility spectometry to detect lung cancer and airway infections. Spectroscopy Europe 18(6), 22–27 (2006)
Bödeker, B., Vautz, W., Baumbach, J.I.: Peak finding and referencing in MCC/IMS-data. International Journal for Ion Mobility Spectrometry 11(1-4), 83–87 (2008)
Delgrande, J.P.: On First-Order Conditional Logics. Artificial Intelligence 105(1-2), 105–137 (1998)
Domingos, P., Lowd, D.: Markov Logic: An Interface Layer for Artificial Intelligence. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan and Claypool, San Rafael (2009)
Domingos, P., Richardson, M.: Markov logic: A unifying framework for statistical relational learning. In: Getoor, L., Taskar, B. (eds.) Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)
Fierens, D., Blockeel, H., Ramon, J., Bruynooghe, M.: Logical Bayesian Networks. In: Dzeroski, S., Blockeel, H. (eds.) Proceedings of the 3rd International Workshop on Multi-Relational Data Mining, pp. 19–30 (2004)
Finthammer, M., Beierle, C., Berger, B., Kern-Isberner, G.: Probabilistic reasoning at optimum entropy with the MEcore system. In: Lane, H.C., Guesgen, H.W. (eds.) Proceedings 22nd International FLAIRS Conference, FLAIRS 2009. AAAI Press, Menlo Park (2009)
Finthammer, M., Beierle, C., Fisseler, J., Kern-Isberner, G., Baumbach, J.I.: Using probabilistic relational learning to support bronchial carcinoma diagnosis based on ion mobility spectrometry. International Journal for Ion Mobility Spectrometry 13, 83–93 (2010)
Getoor, L., Grant, J.: Prl: A probabilistic relational language. Machine Learning 62(1), 7–31 (2006)
Getoor, L., Taskar, B. (eds.): Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)
Jaeger, M.: Relational Bayesian Networks: A Survey. Electronic Transactions in Artificial Intelligence 6 (2002)
Jain, D., Mösenlechner, L., Beetz, M.: Equipping Robot Control Programs with First-Order Probabilistic Reasoning Capabilities. In: International Conference on Robotics and Automation (ICRA), pp. 3130–3135 (2009)
Jain, D., Waldherr, S., Beetz, M.: Bayesian Logic Networks. Tech. rep., IAS Group, Fakultät für Informatik, Technische Universität München (2009)
Kern-Isberner, G.: Characterizing the principle of minimum cross-entropy within a conditional-logical framework. Artificial Intelligence 98, 169–208 (1998)
Kern-Isberner, G.: Conditionals in Nonmonotonic Reasoning and Belief Revision. LNCS (LNAI), vol. 2087. Springer, Heidelberg (2001)
Kern-Isberner, G.: Linking iterated belief change operations to nonmonotonic reasoning. In: Brewka, G., Lang, J. (eds.) Proceedings 11th International Conference on Knowledge Representation and Reasoning, KR 2008, pp. 166–176. AAAI Press, Menlo Park (2008)
Kern-Isberner, G., Thimm, M.: Novel Semantical Approaches to Relational Probabilistic Conditionals. In: Proc. Twelfth International Conference on the Principles of Knowledge Representation and Reasoning (KR 2010), pp. 382–392 (2010)
Kersting, K., De Raedt, L.: Bayesian logic programming: Theory and tool. In: Getoor, L., Taskar, B. (eds.) Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)
Kok, S., Singla, P., Richardson, M., Domingos, P., Sumner, M., Poon, H., Lowd, D., Wang, J.: The Alchemy System for Statistical Relational AI: User Manual. Department of Computer Science and Engineering. University of Washington (2008)
Loh, S., Thimm, M., Kern-Isberner, G.: On the problem of grounding a relational probabilistic conditional knowledge base. In: Proceedings of the 14th International Workshop on Non-Monotonic Reasoning (NMR 2010), Toronto, Canada (May 2010)
Nute, D., Cross, C.: Conditional Logic. In: Gabbay, D., Guenther, F. (eds.) Handbook of Philosophical Logic, vol. 4, pp. 1–98. Kluwer Academic Publishers, Dordrecht (2002)
Paris, J.: The uncertain reasoner’s companion – A mathematical perspective. Cambridge University Press, Cambridge (1994)
Paris, J.: Common sense and maximum entropy. Synthese 117, 75–93 (1999)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1998)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1998)
Raedt, L.D., Kimmig, A., Gutmann, B., Kersting, K., Costa, V.S., Toivonen, H.: Probabilistic Inductive Querying Using ProbLog. Tech. Rep. CW 552, Department of Computer Science, Katholieke Universiteit Leuven, Belgium (June 2009)
Robert Koch-Institut: Public Use File KiGGS, Kinder- und Jugendgesundheitssurvey 2003-2006, Berlin (2008)
Rödder, W., Reucher, E., Kulmann, F.: Features of the expert-system-shell SPIRIT. Logic Journal of the IGPL 14(3), 483–500 (2006)
Schmaußer-Hechfellner, E.: Probabilistische logikbasierte Wissensmodellierung mit statistischen medizinischen Daten unter Verwendung von Lern- und Inferenzverfahren für Markov-Logik-Netze. Bachelor Thesis, Dept. of Computer Science, FernUniversität in Hagen (2011) (in German)
Srinivasan, A.: The Aleph Manual (2007), http://www.comlab.ox.ac.uk/activities/machinelearning/Aleph/
Thimm, M., Finthammer, M., Loh, S., Kern-Isberner, G., Beierle, C.: A system for relational probabilistic reasoning on maximum entropy. In: Guesgen, H.W., Murray, R.C. (eds.) Proceedings 23rd International FLAIRS Conference, FLAIRS 2010, pp. 116–121. AAAI Press, Menlo Park (2010)
Thimm, M., Kern-Isberner, G., Fisseler, J.: Relational probabilistic conditional reasoning at maximum entroy. In: Proceedings ECSQARU-2011 (to appear, 2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kern-Isberner, G., Beierle, C., Finthammer, M., Thimm, M. (2011). Probabilistic Logics in Expert Systems: Approaches, Implementations, and Applications. In: Hameurlain, A., Liddle, S.W., Schewe, KD., Zhou, X. (eds) Database and Expert Systems Applications. DEXA 2011. Lecture Notes in Computer Science, vol 6860. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23088-2_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-23088-2_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23087-5
Online ISBN: 978-3-642-23088-2
eBook Packages: Computer ScienceComputer Science (R0)