ML Anthology

Unsatisfiable Core Shrinking for Anytime Answer Set Optimization

Mario Alviano, Carmine Dodaro
IJCAI 2017 pp. 4781-4785
doi:10.24963/IJCAI.2017/666 /ijcai/2017/alviano2017ijcai-unsatisfiable/

Abstract

Efficient algorithms for the computation of optimum stable models are based on unsatisfiable core analysis. However, these algorithms essentially run to completion, providing few or even no suboptimal stable models. This drawback can be circumvented by shrinking unsatisfiable cores. Interestingly, the resulting anytime algorithm can solve more instances than the original algorithm.

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Text

Alviano and Dodaro. "Unsatisfiable Core Shrinking for Anytime Answer Set Optimization." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/666

Markdown

[Alviano and Dodaro. "Unsatisfiable Core Shrinking for Anytime Answer Set Optimization." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/alviano2017ijcai-unsatisfiable/) doi:10.24963/IJCAI.2017/666

BibTeX

@inproceedings{alviano2017ijcai-unsatisfiable,
  title     = {{Unsatisfiable Core Shrinking for Anytime Answer Set Optimization}},
  author    = {Alviano, Mario and Dodaro, Carmine},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {4781-4785},
  doi       = {10.24963/IJCAI.2017/666},
  url       = {https://mlanthology.org/ijcai/2017/alviano2017ijcai-unsatisfiable/}
}