Possibilistic Answer Set Programming Revisited

Kim Bauters; Steven Schockaert; Martine De Cock; Dirk Vermeir

Possibilistic Answer Set Programming Revisited

Kim Bauters
, Steven Schockaert
, Martine De Cock
, Dirk Vermeir

School of Electronics, Electrical Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

26 Citations (Scopus)

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 semantics are not well-motivated, and do not always yield intuitive results. To develop a more suitable semantics, we first introduce a characterization of answer sets of classical 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 implemented using standard ASP solvers.

Original language	English
Title of host publication	Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)
Editors	Peter Grünwald, Peter Spirtes
Publisher	AUAI Press
Number of pages	8
ISBN (Print)	9780974903965
Publication status	Published - 2010
Event	The 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010) - Catalina Island, California, United States Duration: 08 Jul 2010 → 11 Jul 2010

Conference

Conference	The 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)
Country/Territory	United States
City	California
Period	08/07/2010 → 11/07/2010

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Cite this

@inproceedings{19508aac9a2a4f27a7079ae2bc306485,

title = "Possibilistic Answer Set Programming Revisited",

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 semantics are not well-motivated, and do not always yield intuitive results. To develop a more suitable semantics, we first introduce a characterization of answer sets of classical 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 implemented using standard ASP solvers.",

author = "Kim Bauters and Steven Schockaert and Cock, \{Martine De\} and Dirk Vermeir",

year = "2010",

language = "English",

isbn = "9780974903965",

editor = "Peter Gr{\"u}nwald and Peter Spirtes",

booktitle = "Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)",

publisher = "AUAI Press",

note = "The 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010) ; Conference date: 08-07-2010 Through 11-07-2010",

}

Bauters, K, Schockaert, S, Cock, MD & Vermeir, D 2010, Possibilistic Answer Set Programming Revisited. in P Grünwald & P Spirtes (eds), Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010). AUAI Press, The 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), California, United States, 08/07/2010. <http://event.cwi.nl/uai2010/papers.html>

TY - GEN

T1 - Possibilistic Answer Set Programming Revisited

AU - Bauters, Kim

AU - Schockaert, Steven

AU - Cock, Martine De

AU - Vermeir, Dirk

PY - 2010

Y1 - 2010

N2 - 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 semantics are not well-motivated, and do not always yield intuitive results. To develop a more suitable semantics, we first introduce a characterization of answer sets of classical 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 implemented using standard ASP solvers.

AB - 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 semantics are not well-motivated, and do not always yield intuitive results. To develop a more suitable semantics, we first introduce a characterization of answer sets of classical 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 implemented using standard ASP solvers.

M3 - Conference contribution

SN - 9780974903965

BT - Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)

A2 - Grünwald, Peter

A2 - Spirtes, Peter

PB - AUAI Press

T2 - The 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)

Y2 - 8 July 2010 through 11 July 2010

ER -

Possibilistic Answer Set Programming Revisited

Abstract

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