Abstract
This chapter introduces relational probabilistic graphical models (RPGMs), which combine the expressive power of predicate logic with the uncertain reasoning capabilities of probabilistic graphical models. First, a brief review of propositional and predicate logic is presented. Then, two different relational probabilistic formalisms are described: probabilistic relational models and Markov logic networks. Finally, the application of the two previous approaches is illustrated in two domains, student modeling for a virtual laboratory and visual object recognition based on symbol-relational grammars.
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
References
Domingos, P., Richardson, M.: Markov Logic: A Unifying Framework for Statistical Relational Learning. In: Getoor, L., Taskar, B. (eds.) Introduction to Statistical Relational Learning, pp. 339–371. MIT Press, Cambridge (2007)
Ferrucci, F., Pacini, G., Satta, G., Sessa, M.I., Tortora, G., Tucci, M., Vitiello, G.: Symbol-Relation Grammars: A Formalism for Graphical Languages. Information and Computation 131(1), 1–46 (1996)
Friedman, N., Getoor, L., Koller, D., Pfeffe, A.: Learning Probabilistic Relational Models. In: Proceeding of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1300–1309 (1999)
Genesereth, M.R., Nilsson, N.J.: Logical Foundations of Artificial Intelligence. Morgan Kaufmann (1988)
Getoor, L., Taskar, B.: Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)
Koller D.: Probabilistic Relational Models. In: Proceedings of the 9th International Workshop on Inductive Logic Programming. Lecture Notes in Artificial Intelligence, vol. 1634, Springer, 3–13 (1999)
Lavrac, N., Dzeroski, S.: Inductive Logic Programming: Techniques and Applications. Ellis Horwood, New York (1994)
Lemmon, E.J.: Beginning Logic. Hackett Publishing Company (1978)
Newton-Smith, W.H.: Logic: An Introductory Course. Routledge, Milton Park (1985)
Nienhuys-Cheng, S., de Wolf, R.: Foundations of Inductive Logic Programming. Springer-Verlag, Berlin (1991)
Richardson, M., Domingos, P.: Markov logic networks. Machine Learning. 62(1–2), 107–136 (2006)
Ruiz, E., Sucar, L.E.: An object recognition model based on visual grammars and Bayesian networks. In: Proceedings of the Pacific Rim Symposium on Image and Video Technology, LNCS 8333, pp. 349–359, Springer-Verlag (2014)
Sucar, L.E., Noguez, J.: Student Modeling. In: O. Pourret, P. Naim, B. Marcot (eds.) Bayesian Belief Networks: A Practical Guide to Applications, pp. 173–186. Wiley and Sons (2008)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2015 Springer-Verlag London
About this chapter
Cite this chapter
Sucar, L.E. (2015). Relational Probabilistic Graphical Models. In: Probabilistic Graphical Models. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6699-3_12
Download citation
DOI: https://doi.org/10.1007/978-1-4471-6699-3_12
Published:
Publisher Name: Springer, London
Print ISBN: 978-1-4471-6698-6
Online ISBN: 978-1-4471-6699-3
eBook Packages: Computer ScienceComputer Science (R0)