research-article

Belief Revision in Answer Set Programming

Authors:

Theofanis I. Aravanis,

Pavlos PeppasAuthors Info & Claims

PCI '17: Proceedings of the 21st Pan-Hellenic Conference on Informatics

Article No.: 2, Pages 1 - 5

https://doi.org/10.1145/3139367.3139387

Published: 28 September 2017 Publication History

Abstract

Answer Set Programming is a declarative problem solving approach, initially tailored to modeling problems in the area of Knowledge Representation and Reasoning. In this article, we provide a logic program, in the context of Answer Set Programming framework, that implements the AGM belief revision process, constructed by means of faithful preorders. The above-mentioned approach constitutes a representative implementation of the Answer Set Programming's modeling methodology, as well as a practical method/construction, bringing us a step closer to the successful development of an AGM belief revision system, for real-world applications.

References

[1]

Carlos Alchourrón, Peter Gärdenfors, and David Makinson. 1985. On the logic of theory change: Partial meet contraction and revision functions. Journal of Symbolic Logic 50, 2 (1985), 510--530.

[2]

Theofanis Aravanis, Pavlos Peppas, and Mary-Anne Williams. 2017. Epistemicentrenchment characterization of Parikh's axiom. In Proceedings of the 26th International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia. 772--778.

Digital Library

[3]

Theofanis I. Aravanis, Pavlos Peppas, and Mary-Anne Williams. 2017. Epistemicentrenchment characterization of Parametrized Difference Revision Operators. In Proceedings of the 11th Pan-Hellenic Logic Symposium (PLS 2017). Delphi, Greece, 202--208.

[4]

Alexander Borgida. 1985. Language features for flexible handling of exceptions in information systems. ACM Transactions on Database Systems 10, 4 (1985), 563--603.

Digital Library

[5]

Mukesh Dalal. 1988. Investigations into theory of knowledge base revision: Preliminary report. In Proceedings of 7th National Conference of the American Association for Artificial Intelligence (AAAI '88). 475--479.

Digital Library

[6]

James Delgrande, Torsten Schaub, Hans Tompits, and Stefan Woltran. 2009. A general approach to belief change in Answer Set Programming. ACM Transactions on Computational Logic (2009).

[7]

Peter Gärdenfors. 1988. Knowledge in Flux -- Modeling the Dynamics of Epistemic States. MIT Press, Cambridge, Massachusetts.

[8]

Peter Gärdenfors and David Makinson. 1988. Revisions of knowledge systems using epistemic entrenchment. In Proceedings of the 2nd Conference on Theoretical Aspects of Reasoning About Knowledge. Morgan Kaufmann, Pacific Grove, California, 83--95.

Digital Library

[9]

Martin Gebser, Roland Kaminski, Benjamin Kaufmann, Marius Lindauer, Max Ostrowski, Javier Romero, Torsten Schaub, and Sven Thiele. 2015. Potassco User Guide (2 ed.). Potassco, University of Potsdam.

[10]

Martin Gebser, Ronald Kaminski, Benjamin Kaufmann, and Torsten Schaub. 2012. Answer Set Solving in Practice. Morgan & Claypool Publishers.

Digital Library

[11]

Michael Gelfond. 2008. Answer sets. In Handbook of Knowledge Representation, Frank van Harmelen, Vladimir Lifschitz, and Bruce Porter (Eds.). Elsevier Science, 285--316.

[12]

Michael Gelfond and Vladimir Lifschitz. 1988. The stable model semantics for logic programming. In Proceedings of the 5th International Conference and Symposium of Logic Programming, Robert Kowalski and Kenneth Bowen (Eds.). MIT Press, Pacific Grove, California, 1070--1080.

[13]

Adam Grove. 1988. Two modellings for theory change. Journal of Philosophical Logic 17, 2 (1988), 157--170.

[14]

Hirofumi Katsuno and Alberto Mendelzon. 1991. Propositional knowledge base revision and minimal change. Artificial Intelligence 52, 3 (1991), 263--294.

Digital Library

[15]

Robert Kowalski. 1979. Algorithm = logic + control. Commun. ACM 22 (1979), 424--436.

Digital Library

[16]

Bernhard Nebel. 1998. How hard is it to revise a belief base? In Handbook of Defeasible Reasoning and Uncertainty Management Systems, Vol. 3: Belief Change. Kluwer Academic, 77--145.

[17]

Rohit Parikh. 1999. Beliefs, belief revision, and splitting languages. In Logic, Language and Computation, Lawrence S. Moss, Jonathan Ginzburg, and Maarten de Rijke (Eds.). Vol. 2. CSLI Publications, 266--278.

Digital Library

[18]

Pavlos Peppas. 2008. Belief revision. In Handbook of Knowledge Representation, Frank van Harmelen, Vladimir Lifschitz, and Bruce Porter (Eds.). Elsevier Science, 317--359.

[19]

Pavlos Peppas and Mary-Anne Williams. 2016. Kinetic consistency and relevance in belief revision. In Proceedings of the 15th European Conference on Logics in Artificial Intelligence (JELIA 2016). Springer International Publishing, 401--414.

[20]

Pavlos Peppas, Mary-Anne Williams, Samir Chopra, and Norman Foo. 2015. Relevance in belief revision. Artificial Intelligence 229 (December 2015), 126--138.

Digital Library

[21]

Ken Satoh. 1988. Nonmonotonic reasoning by minimal belief revision. In Proceedings of the International Conference on Fifth Generation Computer Systems. Springer-Verlag, Tokyo, 455--462.

[22]

Mary-Anne Williams. 1992. Two operators for theory bases. In Proceedings of the Australian Joint Conference on Artificial Intelligence. World Scientific, 259--265.

[23]

Mary-Anne Williams. 1993. Transmutations of Knowledge Systems. Ph.D. Dissertation. University of Sydney, Australia.

[24]

Mary-Anne Williams. 1994. On the logic of theory base change. In Logics in Artificial Intelligence -- European Workshop JELIA '94, Craig MacNish, David Pearce, and Luis Pereira (Eds.). Lecture Notes in Artificial Intelligence, Vol. 838. Springer-Verlag Berlin Heidelberg, 86--105.

Digital Library

[25]

Marianne Winslett. 1988. Reasoning about action using a possible models approach. In Proceedings of the 7th AAAI National Conference on Artificial Intelligence (AAAI '88). 89--93.

Digital Library

Cited By

Aravanis T(2021)An ASP-based solver for parametrized-difference revisionJournal of Logic and Computation10.1093/logcom/exab06132:3(630-666)Online publication date: 1-Oct-2021
https://doi.org/10.1093/logcom/exab061
Hunter AAgapeyev J(2019)An Efficient Solver for Parametrized Difference RevisionAI 2019: Advances in Artificial Intelligence10.1007/978-3-030-35288-2_12(143-152)Online publication date: 25-Nov-2019
https://doi.org/10.1007/978-3-030-35288-2_12
Aravanis TPeppas PWilliams M(2018)Iterated Belief Revision and Dalal's OperatorProceedings of the 10th Hellenic Conference on Artificial Intelligence10.1145/3200947.3201038(1-4)Online publication date: 9-Jul-2018
https://dl.acm.org/doi/10.1145/3200947.3201038
Show More Cited By

Index Terms

Belief Revision in Answer Set Programming
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning

Recommendations

General Belief Revision

In artificial intelligence, a key question concerns how an agent may rationally revise its beliefs in light of new information. The standard (AGM) approach to belief revision assumes that the underlying logic contains classical propositional logic. This ...
Iterated Belief Revision and Dalal's Operator
SETN '18: Proceedings of the 10th Hellenic Conference on Artificial Intelligence

One of the main shortcomings of the early AGM paradigm is its lack of any guidelines for iterated revision; it formalizes only one-step rational belief revision. Darwiche and Pearl, subsequently, addressed this problem by introducing four additional ...
Revision by comparison
Special issue on nonmonotonic reasoning

Since the early 1980s, logical theories of belief revision have offered formal methods for the transformation of knowledge bases or "corpora" of data and beliefs. Early models have dealt with unconditional acceptance and integration of potentially ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

PCI '17: Proceedings of the 21st Pan-Hellenic Conference on Informatics

September 2017

322 pages

ISBN:9781450353557

DOI:10.1145/3139367

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Greek Com Soc: Greek Computer Society
University of Thessaly: University of Thessaly, Volos, Greece

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 September 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

PCI 2017

PCI 2017: 21st PAN-HELLENIC CONFERENCE ON INFORMATICS

September 28 - 30, 2017

Larissa, Greece

Acceptance Rates

Overall Acceptance Rate 190 of 390 submissions, 49%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
80
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)0

Reflects downloads up to 14 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Aravanis T(2021)An ASP-based solver for parametrized-difference revisionJournal of Logic and Computation10.1093/logcom/exab06132:3(630-666)Online publication date: 1-Oct-2021
https://doi.org/10.1093/logcom/exab061
Hunter AAgapeyev J(2019)An Efficient Solver for Parametrized Difference RevisionAI 2019: Advances in Artificial Intelligence10.1007/978-3-030-35288-2_12(143-152)Online publication date: 25-Nov-2019
https://doi.org/10.1007/978-3-030-35288-2_12
Aravanis TPeppas PWilliams M(2018)Iterated Belief Revision and Dalal's OperatorProceedings of the 10th Hellenic Conference on Artificial Intelligence10.1145/3200947.3201038(1-4)Online publication date: 9-Jul-2018
https://dl.acm.org/doi/10.1145/3200947.3201038
Aravanis TDemiris KPeppas P(2018)Legal Reasoning in Answer Set Programming2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI.2018.00055(302-306)Online publication date: Nov-2018
https://doi.org/10.1109/ICTAI.2018.00055

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents