author = {Arnaud {Nguembang Fadja} and Fabrizio Riguzzi},
  title = {Probabilistic Logic Programming in Action},
  booktitle = {Towards Integrative Machine Learning and Knowledge Extraction: BIRS Workshop, Banff, AB, Canada, July 24-26, 2015, Revised Selected Papers},
  year = {2017},
  editor = {Andreas Holzinger and Randy Goebel and Massimo Ferri and Vasile Palade},
  publisher = {Springer},
  address = {Heidelberg, \Germany},
  series = {Lecture Notes in Computer Science},
  volume = {10344},
  copyright = {Springer},
  doi = {10.1007/978-3-319-69775-8_5},
  pdf = {},
  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
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.
  pages = {89--116},
  keywords = {Probabilistic Logic Programming, Probabilistic Logical Inference, Hybrid programs},
  scopus = {2-s2.0-85033590324},
  issn = {1860-949X},
  isbn-print = {978-3-319-69774-1},
  isbn-online = {978-3-319-69775-8},
  note = {The final publication is available at Springer via

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