Article details

: Expectation maximization in deep probabilistic logic programming.

: In Chiara Ghidini, Bernardo Magnini, and Andrea Passerini, editors, Proceedings of the 17th Conference of the Italian Association for Artificial Intelligence (AI*IA2018), Trento, Italy, 20-23 November, 2018, volume 11298 of Lecture Notes in Computer Science, pages 293--306, Heidelberg, Germany, 2018. © Springer, Springer. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-030-03840-3_22.

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In this paper, we present an Expectation Maximization algorithm, called Expectation Maximization Parameter learning for HIerarchical Probabilistic Logic programs (EMPHIL) which learns the parameters of Hierarchical Probabilistic Logic Programs from data. The algorithm converts an arithmetic circuit into a Bayesian network and performs the belief propagation algorithm over the corresponding factor graph.

Published at: 17th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2018)

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