@inproceedings{398be6b54f774052a84b50fbacbdc29a,
title = "Distribution-aware sampling of answer sets",
abstract = "Distribution-aware answer set sampling has a wide range of potential applications, for example in the area of probabilistic logic programming or for the computation of approximate solutions of combinatorial or search problems under uncertainty. This paper introduces algorithms for the sampling of answer sets under given probabilistic constraints. Our approaches allow for the specification of probability distributions over stable models using probabilistically weighted facts and rules as constraints for an approximate sampling task with specifiable accuracy. At this, we do not impose any independence requirements on random variables. An experimental evaluation investigates the performance characteristics of the presented algorithms.",
keywords = "Answer set programming (ASP), Nonmonotonic probabilistic logic programming, Probabilistic programming, Relational artificial intelligence",
author = "Matthias Nickles",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 12th International Conference on Scalable Uncertainty Management, SUM 2018 ; Conference date: 03-10-2018 Through 05-10-2018",
year = "2018",
doi = "10.1007/978-3-030-00461-3\_12",
language = "English",
isbn = "9783030004606",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "164--180",
editor = "Davide Ciucci and Gabriella Pasi and Barbara Vantaggi",
booktitle = "Scalable Uncertainty Management - 12th International Conference, SUM 2018, Proceedings",
}