Answer Set Programming (ASP) is a well-established formalism for nonmonotonic reasoning. An ASP program can have no answer set due to cyclic default negation. In this case, it is not possible to draw any conclusion, even if this is not intended. Recently, several paracoherent semantics have been proposed that address this issue,and several potential applications for these semantics have been identified. However, paracoherent semantics have essentially been inapplicable in practice, due to the lack of efficient algorithms and implementations. In this paper, this lack is addressed, and several different algorithms to compute semi-stable and semi-equilibrium models are proposed and implemented into an answer set solving framework. An empirical performance comparison among the new algorithms on benchmarks from ASP competitions is given as well.
|Title of host publication||Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence|
|Subtitle of host publication||(AAAAI-17)|
|Editors||Satinder Singh, Shaul Markovitch|
|Number of pages||7|
|ISBN (Print)||1577357809, 9781577357803|
|Publication status||Published - Jul 2017|
|Event||31st Association for the Advancement of Artificial Intelligence Conference on Artificial Intelligence - Hilton San Francisco Union Square, San Francisco, United States|
Duration: 4 Feb 2017 → 9 Feb 2017
Conference number: 31
https://aaai.org/ocs/index.php/AAAI/AAAI17/index (Link to Conference Details)
|Conference||31st Association for the Advancement of Artificial Intelligence Conference on Artificial Intelligence|
|Period||4/02/17 → 9/02/17|
Amendola, G., Dorado, C., Faber, W., Leone, N., & Ricca, F. (2017). On the Computation of Paracoherent Answer Sets. In S. Singh, & S. Markovitch (Eds.), Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence: (AAAAI-17) (Vol. 1, pp. 1034-1040). AAAI press.