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Expectation Maximization in Deep Probabilistic Logic Programming

  • Arnaud Nguembang Fadja
  • Fabrizio Riguzzi
  • Evelina Lamma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11298)

Abstract

Probabilistic Logic Programming (PLP) combines logic and probability for representing and reasoning over domains with uncertainty. Hierarchical probability Logic Programming (HPLP) is a recent language of PLP whose clauses are hierarchically organized forming a deep neural network or arithmetic circuit. Inference in HPLP is done by circuit evaluation and learning is therefore cheaper than any generic PLP language. We present in this paper an Expectation Maximization algorithm, called Expectation Maximization Parameter learning for HIerarchical Probabilistic Logic programs (EMPHIL), for learning HPLP parameters. The algorithm converts an arithmetic circuit into a Bayesian network and performs the belief propagation algorithm over the corresponding factor graph.

Keywords

Hierarchical probabilistic logic programming Arithmetic circuits Expectation Maximization Factor graph Belief propagation 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Dipartimento di IngegneriaUniversity of FerraraFerraraItaly
  2. 2.Dipartimento di Matematica e InformaticaUniversity of FerraraFerraraItaly

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