Abstract
In the past ten years, the areas of probabilistic inductive logic programming and statistical relational learning put forth a large collection of approaches to combine relational representations of knowledge with probabilistic reasoning. Here, we develop a series of evaluation and comparison criteria for those approaches and focus on the point of view of knowledge representation and reasoning. These criteria address abstract demands such as language aspects, the relationships to propositional probabilistic and first-order logic, and their treatment of information on individuals. We discuss and illustrate the criteria thoroughly by applying them to several approaches to probabilistic relational knowledge representation, in particular, Bayesian logic programs, Markov logic networks, and three approaches based on the principle of maximum entropy.
This research was partially supported by the DFG (BE 1700/7-2 and KE 1413/2-2).
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References
Beierle, C., Kern-Isberner, G.: The relationship of the logic of big-stepped probabilities to standard probabilistic logics. In: Link, S., Prade, H. (eds.) FoIKS 2010. LNCS, vol. 5956, pp. 191–210. Springer, Heidelberg (2010)
Bruynooghe, M., et al.: An Exercise with Statistical Relational Learning Systems. In: Domingos, P., Kersting, K. (eds.) International Workshop on Statistical Relational Learning (SRL 2009), Leuven, Belgium (2009)
De Raedt, L., Kersting, K.: Probabilistic inductive logic programming. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S.H. (eds.) Probabilistic Inductive Logic Programming. LNCS (LNAI), vol. 4911, pp. 1–27. Springer, Heidelberg (2008)
Delgrande, J.: On first-order conditional logics. Artificial Intelligence 105, 105–137 (1998)
Domingos, P., Lowd, D.: Markov Logic: An Interface Layer for Artificial Intelligence. In: Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan and Claypool, San Rafael (2009)
Fisseler, J.: Learning and Modeling with Probabilistic Conditional Logic. Dissertations in Artificial Intelligence, vol. 328. IOS Press, Amsterdam (2010)
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)
Jaeger, M.: Model-Theoretic Expressivity Analysis. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S.H. (eds.) Probabilistic Inductive Logic Programming. LNCS (LNAI), vol. 4911, pp. 325–339. Springer, Heidelberg (2008)
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)
Kern-Isberner, G.: Characterizing the principle of minimum cross-entropy within a conditional-logical framework. Artificial Intelligence 98, 169–208 (1998)
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)
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)
Paris, J.B.: The uncertain reasoner’s companion – A mathematical perspective. Cambridge University Press, Cambridge (1994)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1998)
Poole, D.: First-order probabilistic inference. In: Gottlob, G., Walsh, T. (eds.) Proc. IJCAI 2003, pp. 985–991. Morgan Kaufmann, San Francisco (2003)
Singla, P., Domingos, P.: Lifted first-order belief propagation. In: Fox, D., Gomes, C.P. (eds.) Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, pp. 1094–1099. AAAI Press/The MIT Press (2008)
Thimm, M., Finthammer, M., Kern-Isberner, G., Beierle, C.: Comparing approaches to relational probabilistic reasoning: Theory and implementation (2010) (submitted)
Thimm, M., Kern-Isberner, G., Fisseler, J.: Relational probabilistic conditional reasoning at maximum entropy. In: Liu, W. (ed.) ECSQARU 2011. LNCS, vol. 6717, pp. 447–458. Springer, Heidelberg (2011)
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Beierle, C., Finthammer, M., Kern-Isberner, G., Thimm, M. (2011). Evaluation and Comparison Criteria for Approaches to Probabilistic Relational Knowledge Representation. In: Bach, J., Edelkamp, S. (eds) KI 2011: Advances in Artificial Intelligence. KI 2011. Lecture Notes in Computer Science(), vol 7006. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24455-1_6
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