Abduction with probabilistic logic programming under the distribution semantics

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...

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Bibliographic Details
Published in:International journal of approximate reasoning Vol. 142; pp. 41 - 63
Main Authors: Azzolini, Damiano, Bellodi, Elena, Ferilli, Stefano, Riguzzi, Fabrizio, Zese, Riccardo
Format: Journal Article
Language:English
Published: Elsevier Inc 01.03.2022
Subjects:
Abduction
Distribution semantics
Probabilistic logic programming
Statistical relational artificial intelligence
Statistical relational artificial intelligence
Distribution semantics
Probabilistic logic programming
Abduction
ISSN:0888-613X, 1873-4731
Online Access:Get full text
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Summary: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.
ISSN:0888-613X
1873-4731
DOI:10.1016/j.ijar.2021.11.003