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Probabilistic Logic Programming in Action

  • Arnaud Nguembang Fadja
  • Fabrizio Riguzzi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10344)

Abstract

Probabilistic Programming (PP) has recently emerged as an effective approach for building complex probabilistic models. Until recently PP was mostly focused on functional programming while now Probabilistic Logic Programming (PLP) forms a significant subfield. In this paper we aim at presenting a quick overview of the features of current languages and systems for PLP. We first present the basic semantics for probabilistic logic programs and then consider extensions for dealing with infinite structures and continuous random variables. To show the modeling features of PLP in action, we present several examples: a simple generator of random 2D tile maps, an encoding of Markov Logic Networks, the truel game, the coupon collector problem, the one-dimensional random walk, latent Dirichlet allocation and the Indian GPA problem. These examples show the maturity of PLP.

Keywords

Probabilistic Logic Programming Probabilistic logical inference Hybrid program 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

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

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