Answer Set Programming (ASP) is a well established formalism for nonmonotonic reasoning. While incoherence, the non-existence of answer sets for some programs, is an important feature of ASP, it has frequently been criticised and indeed has some disadvantages, especially for query answering. Paracoherent semantics have been suggested as a remedy, which extend the classical notion of answer sets to draw meaningful conclusions also from incoherent programs. In this paper we present an alternative characterization of the two major paracoherent semantics in terms of (extended) externally supported models. This definition uses a transformation of ASP programs that is more parsimonious than the classic epistemic transformation used in recent implementations. A performance comparison carried out on benchmarks from ASP competitions shows that the usage of the new transformation brings about performance improvements that are independent of the underlying algorithms.
|Title of host publication||Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence|
|Editors||Sheila McIlraith, Kilian Weinberger|
|Number of pages||8|
|Publication status||Published - 25 Apr 2018|
|Event||32nd Association for the Advancement of Artificial Intelligence Conference - Hilton New Orleans Riverside, New Orleans, United States|
Duration: 2 Feb 2018 → 7 Feb 2018
Conference number: 32
https://aaai.org/Conferences/AAAI-18/ (Link to Conference Details)
|Conference||32nd Association for the Advancement of Artificial Intelligence Conference|
|Period||2/02/18 → 7/02/18|
Amendola, G., Dodaro, C., Faber, W., & Ricca, F. (2018). Externally Supported Models for Efficient Computation of Paracoherent Answer Sets. In S. McIlraith, & K. Weinberger (Eds.), Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence AAAI press.