Abstract
Abduction is one of the basic logical inferences (deduction, induction and abduction) and derives the best explanations for our observation. Statistical abduction attempts to define a probability distribution over explanations and to evaluate them by their probabilities. Logic-based probabilistic models (LBPMs) have been developed as a way to combine probabilities and logic, and it enables us to perform statistical abduction. However non-deterministic knowledge like preference and frequency seems difficult to represent by logic. Bayesian inference can reflect such knowledge on a prior distribution, and variational Bayes (VB) is known as an approximation method for it. In this paper, we propose VB for logic-based probabilistic models and show that our proposed method is efficient in evaluating abductive explanations about failure in a logic circuit and a metabolic pathway.
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
Poole, D.: Probabilistic Horn abduction and Bayesian networks. Artificial Intelligence 64(1), 81–129 (1993)
Ishihata, M., Kameya, Y., Sato, T., Minato, S.: An EM algorithm on BDDs with order encoding for logic-based probabilistic models. In: Proc. of ACML 2010 (2010)
Singla, P., Mooney, R.J.: Abductive Markov Logic for Plan Recognition. In: Proc. of AAAI 2011 (2011)
Raghavan, S., Mooney, R.J.: Abductive Plan Recognition by Extending Bayesian Logic Programs. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011. LNCS, vol. 6912, pp. 629–644. Springer, Heidelberg (2011)
Inoue, K., Sato, T., Ishihata, M., Kameya, Y., Nabeshima, H.: Evaluating abductive hypotheses using an EM algorithm on BDDs. In: Proc. of IJCAI 2009 (2009)
Synnaeve, G., Inoue, K., Doncescu, A., Nabeshima, H., Kameya, Y., Ishihata, M., Sato, T.: Kinetic Models and Qualitative Abstraction for Relational Learning in Systems Biology. In: BIOSTEC Bioinformatics 2011 (2011)
Getoor, L., Taskar, B.: Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning). The MIT Press (2007)
Muggleton, S.: Stochastic Logic Programs. In: New Generation Computing. Academic Press (1996)
Poole, D.: The Independent Choice Logic for modelling multiple agents under uncertainty. Artificial Intelligence 94, 7–56 (1997)
Sato, T., Kameya, Y.: Parameter Learning of Logic Programs for Symbolic-statistical Modeling. Journal of Artificial Intelligence Research 15, 391–454 (2001)
Kersting, K., De Raedt, L.: Towards Combining Inductive Logic Programming with Bayesian Networks. In: Rouveirol, C., Sebag, M. (eds.) ILP 2001. LNCS (LNAI), vol. 2157, pp. 118–131. Springer, Heidelberg (2001)
Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62(1), 107–136 (2006)
De Raedt, L., Kimming, A., Toivonen, H.: ProbLog: A probabilistic Prolog and its application in link discovery. In: Proc. of IJCAI 2007, pp. 2468–2473 (2007)
Raghavan, S., Mooney, R.: Bayesian Abductive Logic Programs. In: Proc. of AAAI 2010 Workshop on Star-AI 2010 (2010)
Sato, T., Kameya, Y., Kurihara, K.: Variational Bayes via propositionalized probability computation in PRISM. Annals of Mathematics and Artificial Inteligence 54(1-3), 135–158 (2009)
Sato, T.: A General MCMC Method for Bayesian Inference in Logic-based Probabilistic Modeling. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 (2011)
Beal, M., Ghahramani, Z.: The Variational Bayesian EM Algorithm for Incomplete Data: with Application to Scoring Graphical Model Structures. Bayesian Statistics 7 (2003)
Akers, S.B.: Binary decision diagrams. IEEE Transaction on Computers 27(6), 509–516 (1978)
Tamaddoni-Nezhad, A., Chaleil, R., Kakas, A., Muggleton, S.: Application of abductive ILP to learning metabolic network inhibition from temporal data. Machine Learning 64, 209–230 (2006)
Gutmann, B., Thon, I., De Raedt, L.: Learning the parameters of probabilistic logic programs from interpretations, Dept. of Computer Science, K.U.Leuven (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ishihata, M., Kameya, Y., Sato, T. (2012). Variational Bayes Inference for Logic-Based Probabilistic Models on BDDs. In: Muggleton, S.H., Tamaddoni-Nezhad, A., Lisi, F.A. (eds) Inductive Logic Programming. ILP 2011. Lecture Notes in Computer Science(), vol 7207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31951-8_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-31951-8_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31950-1
Online ISBN: 978-3-642-31951-8
eBook Packages: Computer ScienceComputer Science (R0)