Abstract
We describe a prototypical software framework for probabilistic inductive logic programming which supports the seamless combination of non-monotonic reasoning, probabilistic inference and parameter learning. While building upon existing as well as new approaches to probabilistic Answer Set Programming, our framework distinguishes itself from related works by placing virtually no restrictions on the annotation of knowledge with probabilities. User-configurable algorithms provide for general as well as specialized, scalable approaches to inference and parameter learning, allowing for adaptability with regard to complex reasoning and weight learning tasks.
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References
Sato, T., Kameya, Y.: Prism: a language for symbolic-statistical modeling. In: Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI 97) (1997)
Kersting, K., Raedt, L.D.: Bayesian logic programs. In: Proceedings of the 10th International Conference on Inductive Logic Programming (2000)
Raedt, L.D., Kimmig, A., Toivonen, H.: Problog: A probabilistic prolog and its application in link discovery. In: IJCAI, pp. 2462–2467 (2007)
Poole, D.: The independent choice logic for modelling multiple agents under uncertainty. Artif. Intell. 94, 7–56 (1997)
Richardson, M., Domingos, P.: Markov logic networks. Mach. Learn. 62, 107–136 (2006)
Ng, R.T., Subrahmanian, V.S.: Stable semantics for probabilistic deductive databases. Inf. Comput. 110, 42–83 (1994)
Baral, C., Gelfond, M., Rushton, N.: Probabilistic reasoning with answer sets. Theory Pract. Log. Program. 9, 57–144 (2009)
Saad, E., Pontelli, E.: Hybrid probabilistic logic programs with non-monotonic negation. In: Gabbrielli, M., Gupta, G. (eds.) ICLP 2005. LNCS, vol. 3668, pp. 204–220. Springer, Heidelberg (2005)
Corapi, D., Sykes, D., Inoue, K., Russo, A.: Probabilistic rule learning in nonmonotonic domains. In: Leite, J., Torroni, P., Ågotnes, T., Boella, G., van der Torre, L. (eds.) CLIMA XII 2011. LNCS, vol. 6814, pp. 243–258. Springer, Heidelberg (2011)
Goodman, N.D., Mansinghka, V.K., Roy, D.M., Bonawitz, K., Tenenbaum, J.B.: Church: a language for generative models. In: Proceedings of Uncertainty in Artificial Intelligence (2008)
Pfeffer, A.: Figaro: An object-oriented probabilistic programming language. In: Charles River Analytics Technical report (2009)
Nickles, M., Mileo, A.: Probabilistic inductive logic programming based on answer set programming. In: 15th International Workshop on Non-Monotonic Reasoning (NMR 2014) (2014)
Lee, J., Palla, R.: System f2lp – Computing Answer Sets of First-Order Formulas. In: Erdem, E., Lin, F., Schaub, T. (eds.) LPNMR 2009. LNCS, vol. 5753, pp. 515–521. Springer, Heidelberg (2009)
Rodder, W., Meyer, C.: Coherent knowledge processing at maximum entropy by spirit. In: Proceedings of 12th Conference on Uncertainty in Artificial Intelligence (UAI 1996) (1996)
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Nickles, M., Mileo, A. (2015). A System for Probabilistic Inductive Answer Set Programming. In: Beierle, C., Dekhtyar, A. (eds) Scalable Uncertainty Management. SUM 2015. Lecture Notes in Computer Science(), vol 9310. Springer, Cham. https://doi.org/10.1007/978-3-319-23540-0_7
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DOI: https://doi.org/10.1007/978-3-319-23540-0_7
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