The complexity of inferences and explanations in probabilistic logic programming

Fabio G. Cozman, Denis D. Mauá

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferencia

1 Cita (Scopus)

Resumen

© Springer International Publishing AG 2017. A popular family of probabilistic logic programming languages combines logic programs with independent probabilistic facts. We study the complexity of marginal inference, most probable explanations, and maximum a posteriori calculations for propositional/relational probabilistic logic programs that are acyclic/definite/stratified/normal/ disjunctive. We show that complexity classes Σk and PPΣk (for various values of k) and NPPP are all reached by such computations.
Idioma originalInglés estadounidense
Título de la publicación alojadaThe complexity of inferences and explanations in probabilistic logic programming
Páginas449-458
Número de páginas10
ISBN (versión digital)9783319615806
DOI
EstadoPublicada - 1 ene 2017
Publicado de forma externa
EventoLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duración: 1 ene 2018 → …

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen10369 LNAI
ISSN (versión impresa)0302-9743

Conferencia

ConferenciaLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Período1/01/18 → …

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  • Citar esto

    Cozman, F. G., & Mauá, D. D. (2017). The complexity of inferences and explanations in probabilistic logic programming. En The complexity of inferences and explanations in probabilistic logic programming (pp. 449-458). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10369 LNAI). https://doi.org/10.1007/978-3-319-61581-3_40