ML Anthology

Evaluating Epistemic Negation in Answer Set Programming (Extended Abstract)

Yi-Dong Shen, Thomas Eiter
IJCAI 2017 pp. 5060-5064
doi:10.24963/IJCAI.2017/722 /ijcai/2017/shen2017ijcai-evaluating/

Abstract

Epistemic negation 'not' along with default negation 'neg' plays a key role in knowledge representation and nonmonotonic reasoning. However, the existing approaches behave not satisfactorily in that they suffer from the problems of unintended world views due to recursion through the epistemic modal operator K or M ( K F and M F are shorthands for (neg not F) and (not neg F), respectively). In this paper we present a general approach to epistemic negation which is free of unintended world views and thus offers a solution to the long-standing problem of epistemic specifications which were introduced by Gelfond 1991 over two decades ago.

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Text

Shen and Eiter. "Evaluating Epistemic Negation in Answer Set Programming (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/722

Markdown

[Shen and Eiter. "Evaluating Epistemic Negation in Answer Set Programming (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/shen2017ijcai-evaluating/) doi:10.24963/IJCAI.2017/722

BibTeX

@inproceedings{shen2017ijcai-evaluating,
  title     = {{Evaluating Epistemic Negation in Answer Set Programming (Extended Abstract)}},
  author    = {Shen, Yi-Dong and Eiter, Thomas},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {5060-5064},
  doi       = {10.24963/IJCAI.2017/722},
  url       = {https://mlanthology.org/ijcai/2017/shen2017ijcai-evaluating/}
}