Abstract
An important goal of statistical relational learning formalisms is to develop representations that are compact and expressive but also easy to read and maintain. This is can be achieved by exploiting the modularity of rule-based structures and is related to the noisy-or structure where parents independently influence a joint effect. Typically, these rules are combined in an additive manner where a new rule increases the probability of the effect. In this paper, we present a new language feature for CP-logic, where we allow negation in the head of rules to express the inhibition of an effect in a modular manner. This is a generalization of the inhibited noisy-or structure that can deal with cycles and, foremost, is non-conflicting. We introduce syntax and semantics for this feature and show how this is a natural counterpart to the standard noisy-or. Experimentally, we illustrate that in practice there is no additional cost when performing inference compared to a noisy-or structure.
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References
Buchanan, B.G., Shortlife, E.H.: Rule Based Expert Systems: The MYCIN Experimehts of the Stanford Heuristic Programming Project. Addison-Wesley (1984)
De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S. (eds.): Probabilistic Inductive Logic Programming. LNCS (LNAI), vol. 4911. Springer, Heidelberg (2008)
Denecker, M., Ternovska, E.: Inductive situation calculus. Artificial Intelligence 171(5-6), 332–360 (2007)
Díez, F.J., Druzdzel, M.: Canonical probabilistic models for knowledge engineering. Technical report cisiad-06-01, UNED, Madrid, Spain (2006)
Díez, F.J., Galán, S.F.: Efficient computation for the noisy max. International Journal of Intelligent Systems 18(2), 165–177 (2003)
Fierens, D., Van den Broeck, G., Thon, I., Gutmann, B., De Raedt, L.: Inference in probabilistic logic programs using weighted CNFs. arXiv preprint arXiv:1202.3719 (2012)
Getoor, L., Taskar, B. (eds.): Statistical Relational Learning. MIT Press (2007)
Heckerman, D.E., Shortliffe, E.H.: From certainty factors to belief networks. Artificial Intelligence in Medicine 4(1), 35–52 (1992)
Hommersom, A., Lucas, P.J.F.: Generalising the interaction rules in probabilistic logic. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 2, pp. 912–917 (2011)
Kuter, U., Nau, D., Gossink, D., Lemmer, J.F.: Interactive course-of-action planning using causal models. In: Third International Conference on Knowledge Systems for Coalition Operations, KSCO 2004 (2004)
Lucas, P., Van Der Gaag, L.: Principles of expert systems. Addison-Wesley Longman Publishing Co., Inc. (1991)
Meert, W., Struyf, J., Blockeel, H.: Learning ground CP-logic theories by leveraging Bayesian network learning techniques. Fundamenta Informaticae 89(1), 131–160 (2008)
Pearl, J.: Causality: Models, Reasoning, and Inference. Cambridge University Press (2000)
Sato, T., Kameya, Y.: New advances in logic-based probabilistic modeling by PRISM. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S.H. (eds.) Probabilistic Inductive Logic Programming. LNCS (LNAI), vol. 4911, pp. 118–155. Springer, Heidelberg (2008)
Takikawa, M., D’Ambrosio, B.: Multiplicative factorization of noisy-max. In: Laskey, K.B., Prade, H. (eds.) Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence (UAI), pp. 622–630 (1999)
Vennekens, J., Bruynooghe, M., Denecker, M.: Embracing events in causal modelling: Interventions and counterfactuals in CP-logic. In: Janhunen, T., Niemelä, I. (eds.) JELIA 2010. LNCS (LNAI), vol. 6341, pp. 313–325. Springer, Heidelberg (2010)
Vennekens, J., Denecker, M., Bruynooghe, M.: CP-logic: A language of causal probabilistic events and its relation to logic programming. Theory and Practice of Logic Programming (TPLP) 9(3), 245–308 (2009)
Zagorecki, A., Druzdzel, M.J.: An empirical study of probability elicitation under noisy-or assumption. In: FLAIRS Conference, pp. 880–886 (2004)
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Meert, W., Vennekens, J. (2014). Inhibited Effects in CP-Logic. In: van der Gaag, L.C., Feelders, A.J. (eds) Probabilistic Graphical Models. PGM 2014. Lecture Notes in Computer Science(), vol 8754. Springer, Cham. https://doi.org/10.1007/978-3-319-11433-0_23
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DOI: https://doi.org/10.1007/978-3-319-11433-0_23
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