Abstraction for Non-Ground Answer Set Programs (Extended Abstract)

Zeynep G. Saribatur, Thomas Eiter, Peter Schüller

IJCAI 2022 pp. 5767-5771

doi:10.24963/IJCAI.2022/807 /ijcai/2022/saribatur2022ijcai-abstraction/

Abstract

Abstraction is a powerful technique that has not been considered much for nonmonotonic reasoning formalisms including Answer Set Programming (ASP), apart from related simplification methods. We introduce a notion for abstracting from the domain of an ASP program that shrinks the domain size and over-approximates the set of answer sets, as well as an abstraction-&-refinement methodology that, starting from an initial abstraction, automatically yields an abstraction with an associated answer set matching an answer set of the original program if one exists. Experiments reveal the potential of the approach, by its ability to focus on the program parts that cause unsatisfiability and by achieving concrete abstract answer sets that merely reflect relevant details.

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Text

Saribatur et al. "Abstraction for Non-Ground Answer Set Programs (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/807

Markdown

[Saribatur et al. "Abstraction for Non-Ground Answer Set Programs (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/saribatur2022ijcai-abstraction/) doi:10.24963/IJCAI.2022/807

BibTeX

@inproceedings{saribatur2022ijcai-abstraction,
  title     = {{Abstraction for Non-Ground Answer Set Programs (Extended Abstract)}},
  author    = {Saribatur, Zeynep G. and Eiter, Thomas and Schüller, Peter},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {5767-5771},
  doi       = {10.24963/IJCAI.2022/807},
  url       = {https://mlanthology.org/ijcai/2022/saribatur2022ijcai-abstraction/}
}