The most probable explanation for probabilistic logic programs with annotated disjunctions

Dimitar Shterionov*, Joris Renkens, Jonas Vlasselaer, Angelika Kimmig, Wannes Meert, Gerda Janssens

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

7 Citations (Scopus)


Probabilistic logic languages, such as ProbLog and CP-logic, are probabilistic generalizations of logic programming that allow one to model probability distributions over complex, structured domains. Their key probabilistic constructs are probabilistic facts and annotated disjunctions to represent binary and mutli-valued random variables, respectively. ProbLog allows the use of annotated disjunctions by translating them into probabilistic facts and rules. This encoding is tailored towards the task of computing the marginal probability of a query given evidence (MARG), but is not correct for the task of finding the most probable explanation (MPE) with important applications e.g., diagnostics and scheduling. In this work, we propose a new encoding of annotated disjunctions which allows correct MARG and MPE. We explore from both theoretical and experimental perspective the trade-off between the encoding suitable only for MARG inference and the newly proposed (general) approach.

Original languageEnglish
Title of host publicationInductive Logic Programming - 24th International Conference, ILP 2014, Revised Selected Papers
EditorsJesse Davis, Jan Ramon
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783319237077
Publication statusPublished - 2015
Externally publishedYes
Event24th International Conference on Inductive Logic Programming, ILP 2014 - Nancy, France
Duration: 14 Sept 201416 Sept 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th International Conference on Inductive Logic Programming, ILP 2014


  • Logic programs with annotated disjunctions
  • Most Probable Explanation
  • Probabilistic logic programming
  • ProbLog
  • Statistical relational learning


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