Externally Supported Models for Efficient Computation of Paracoherent Answer Sets

Giovanni Amendola; Carmine Dodaro; Wolfgang Faber; Francesco Ricca

doi:10.1609/aaai.v32i1.11540

Externally Supported Models for Efficient Computation of Paracoherent Answer Sets

Giovanni Amendola, Carmine Dodaro, Wolfgang Faber, Francesco Ricca

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

13 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence
Subtitle of host publication	(AAAI 2018)
Editors	Sheila McIlraith, Kilian Weinberger
Publisher	AAAI press
Pages	1720-1727
Number of pages	8
Volume	1
ISBN (Print)	9781577358008
DOIs	https://doi.org/10.1609/aaai.v32i1.11540
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)

Publication series

Name	Proceedings of the AAAI Conference on Artificial Intelligence
Publisher	AAAI
Number	1
Volume	32
ISSN (Print)	2159-5399
ISSN (Electronic)	2374-3468

Conference

Conference	32nd Association for the Advancement of Artificial Intelligence Conference
Abbreviated title	AAAI-18
Country/Territory	United States
City	New Orleans
Period	2/02/18 → 7/02/18
Internet address	https://aaai.org/Conferences/AAAI-18/ (Link to Conference Details)

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10.1609/aaai.v32i1.11540Licence: Unspecified

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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 2018) (Vol. 1, pp. 1720-1727). (Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 32, No. 1). AAAI press. https://doi.org/10.1609/aaai.v32i1.11540

Amendola, Giovanni ; Dodaro, Carmine ; Faber, Wolfgang et al. / Externally Supported Models for Efficient Computation of Paracoherent Answer Sets. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence: (AAAI 2018). editor / Sheila McIlraith ; Kilian Weinberger. Vol. 1 AAAI press, 2018. pp. 1720-1727 (Proceedings of the AAAI Conference on Artificial Intelligence; 1).

@inproceedings{e105bdf4df314bf6b538638b645d39be,

title = "Externally Supported Models for Efficient Computation of Paracoherent Answer Sets",

abstract = "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.",

keywords = "Answer Set Programming (ASP), Artificial intelligence, Benchmarking, Logic programming, Semantics, Knowledge Representation and Reasoning, Paracoherent Answer Sets",

author = "Giovanni Amendola and Carmine Dodaro and Wolfgang Faber and Francesco Ricca",

note = "Copyright {\textcopyright} 2018, Association for the Advancement of Artiﬁcial Intelligence (www.aaai.org). All rights reserved; 32nd Association for the Advancement of Artificial Intelligence Conference , AAAI-18 ; Conference date: 02-02-2018 Through 07-02-2018",

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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 2018). vol. 1, Proceedings of the AAAI Conference on Artificial Intelligence, no. 1, vol. 32, AAAI press, pp. 1720-1727, 32nd Association for the Advancement of Artificial Intelligence Conference , New Orleans, Louisiana, United States, 2/02/18. https://doi.org/10.1609/aaai.v32i1.11540

Externally Supported Models for Efficient Computation of Paracoherent Answer Sets. / Amendola, Giovanni; Dodaro, Carmine; Faber, Wolfgang et al.

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence: (AAAI 2018). ed. / Sheila McIlraith; Kilian Weinberger. Vol. 1 AAAI press, 2018. p. 1720-1727 (Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 32, No. 1).

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

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T1 - Externally Supported Models for Efficient Computation of Paracoherent Answer Sets

AU - Amendola, Giovanni

AU - Dodaro, Carmine

AU - Faber, Wolfgang

AU - Ricca, Francesco

N1 - Conference code: 32

PY - 2018/4/25

Y1 - 2018/4/25

N2 - 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.

AB - 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.

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KW - Semantics

KW - Knowledge Representation and Reasoning

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T3 - Proceedings of the AAAI Conference on Artificial Intelligence

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EP - 1727

BT - Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence

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T2 - 32nd Association for the Advancement of Artificial Intelligence Conference

Y2 - 2 February 2018 through 7 February 2018

ER -

Amendola G, Dodaro C, Faber W, Ricca F. Externally Supported Models for Efficient Computation of Paracoherent Answer Sets. In McIlraith S, Weinberger K, editors, Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence: (AAAI 2018). Vol. 1. AAAI press. 2018. p. 1720-1727. (Proceedings of the AAAI Conference on Artificial Intelligence; 1). doi: 10.1609/aaai.v32i1.11540

Externally Supported Models for Efficient Computation of Paracoherent Answer Sets

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