Abstract
We present ProbLog2, the state of the art implementation of the probabilistic programming language ProbLog. The ProbLog language allows the user to intuitively build programs that do not only encode complex interactions between a large sets of heterogenous components but also the inherent uncertainties that are present in real-life situations. The system provides efficient algorithms for querying such models as well as for learning their parameters from data. It is available as an online tool on the web and for download. The offline version offers both command line access to inference and learning and a Python library for building statistical relational learning applications from the system’s components.
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De Raedt, L., Kimmig, A.: Probabilistic (logic) programming concepts. Machine Learning (2015)
De Raedt, L., Kimmig, A., Toivonen, H.: ProbLog: a probabilistic prolog and its application in link discovery. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI) (2007)
Fierens, D., Van den Broeck, G., Renkens, J., Shterionov, D., Gutmann, B., Thon, I., Janssens, G., De Raedt, L.: Inference and learning in probabilistic logic programs using weighted Boolean formulas. Theory and Practice of Logic Programming 15(03), 358–401 (2015)
Vlasselaer, J., Van den Broeck, G., Kimmig, A., Meert, W., De Raedt, L.: Anytime inference in probabilistic logic programs with Tp-compilation. In: Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI) (2015)
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Dries, A. et al. (2015). ProbLog2: Probabilistic Logic Programming. In: Bifet, A., et al. Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science(), vol 9286. Springer, Cham. https://doi.org/10.1007/978-3-319-23461-8_37
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DOI: https://doi.org/10.1007/978-3-319-23461-8_37
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