title = {Abduction with probabilistic logic programming under the distribution semantics},
  journal = {International Journal of Approximate Reasoning},
  volume = {142},
  pages = {41-63},
  year = {2022},
  issn = {0888-613X},
  doi = {10.1016/j.ijar.2021.11.003},
  url = {https://www.sciencedirect.com/science/article/pii/S0888613X2100181X},
  author = {Damiano Azzolini and Elena Bellodi and Stefano Ferilli and Fabrizio Riguzzi and Riccardo Zese},
  keywords = {Abduction, Distribution semantics, Probabilistic logic programming, Statistical relational artificial intelligence},
  abstract = {In Probabilistic Abductive Logic Programming we are given a probabilistic logic program, a set of abducible facts, and a set of constraints. Inference in probabilistic abductive logic programs aims to find a subset of the abducible facts that is compatible with the constraints and that maximizes the joint probability of the query and the constraints. In this paper, we extend the PITA reasoner with an algorithm to perform abduction on probabilistic abductive logic programs exploiting Binary Decision Diagrams. Tests on several synthetic datasets show the effectiveness of our approach.},
  scopus = {2-s2.0-85119493622}

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