Possibilistic Answer Set Programming Revisited

Kim Bauters, Steven Schockaert, Martine De Cock, Dirk Vermeir
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:48-55, 2010.

Abstract

Possibilistic answer set programming (PASP) extends answer set programming (ASP) by attaching to each rule a degree of certainty. While such an extension is important from an application point of view, existing seman- tics are not well-motivated, and do not al- ways yield intuitive results. To develop a more suitable semantics, we first introduce a characterization of answer sets of classi- cal ASP programs in terms of possibilistic logic where an ASP program specifies a set of constraints on possibility distributions. This characterization is then naturally generalized to define answer sets of PASP programs. We furthermore provide a syntactic counterpart, leading to a possibilistic generalization of the well-known Gelfond-Lifschitz reduct, and we show how our framework can readily be im- plemented using standard ASP solvers.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR8-bauters10a, title = {Possibilistic Answer Set Programming Revisited}, author = {Bauters, Kim and Schockaert, Steven and De Cock, Martine and Vermeir, Dirk}, booktitle = {Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence}, pages = {48--55}, year = {2010}, editor = {Grünwald, Peter and Spirtes, Peter}, volume = {R8}, series = {Proceedings of Machine Learning Research}, month = {08--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r8/main/assets/bauters10a/bauters10a.pdf}, url = {https://proceedings.mlr.press/r8/bauters10a.html}, abstract = {Possibilistic answer set programming (PASP) extends answer set programming (ASP) by attaching to each rule a degree of certainty. While such an extension is important from an application point of view, existing seman- tics are not well-motivated, and do not al- ways yield intuitive results. To develop a more suitable semantics, we first introduce a characterization of answer sets of classi- cal ASP programs in terms of possibilistic logic where an ASP program specifies a set of constraints on possibility distributions. This characterization is then naturally generalized to define answer sets of PASP programs. We furthermore provide a syntactic counterpart, leading to a possibilistic generalization of the well-known Gelfond-Lifschitz reduct, and we show how our framework can readily be im- plemented using standard ASP solvers.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Possibilistic Answer Set Programming Revisited %A Kim Bauters %A Steven Schockaert %A Martine De Cock %A Dirk Vermeir %B Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2010 %E Peter Grünwald %E Peter Spirtes %F pmlr-vR8-bauters10a %I PMLR %P 48--55 %U https://proceedings.mlr.press/r8/bauters10a.html %V R8 %X Possibilistic answer set programming (PASP) extends answer set programming (ASP) by attaching to each rule a degree of certainty. While such an extension is important from an application point of view, existing seman- tics are not well-motivated, and do not al- ways yield intuitive results. To develop a more suitable semantics, we first introduce a characterization of answer sets of classi- cal ASP programs in terms of possibilistic logic where an ASP program specifies a set of constraints on possibility distributions. This characterization is then naturally generalized to define answer sets of PASP programs. We furthermore provide a syntactic counterpart, leading to a possibilistic generalization of the well-known Gelfond-Lifschitz reduct, and we show how our framework can readily be im- plemented using standard ASP solvers. %Z Reissued by PMLR on 04 October 2026.
APA
Bauters, K., Schockaert, S., De Cock, M. & Vermeir, D.. (2010). Possibilistic Answer Set Programming Revisited. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R8:48-55 Available from https://proceedings.mlr.press/r8/bauters10a.html. Reissued by PMLR on 04 October 2026.

Related Material