Belief change and non-monotonic reasoning sans compactness

Jandson S. Ribeiro, Abhaya Nayak, Renata Wassermann

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionResearchpeer-review

Abstract

Belief change and non-monotonic reasoning are arguably different perspectives on the same phenomenon, namely, jettisoning of currently held beliefs in response to some incompatible evidence. Investigations in this area typically assume, among other things, that the underlying (background) logic is compact, that is, whatever can be inferred from a set of sentences X can be inferred from a finite subset of X. Recent research in the field shows that this compactness assumption can be dispensed without inflicting much damage on the AGM paradigm of belief change. In this paper we investigate the impact of such relaxation on non-monotonic logics instead. In particular, we show that, when compactness is not guaranteed, while the bridge from the AGM paradigm of belief change to expectation logics remains unaffected, the “return trip” from expectation logics to AGM paradigm is no longer guaranteed. We finally explore the conditions under which such guarantee can be given.
LanguageEnglish
Title of host publicationProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence
Subtitle of host publicationAAAI 2019
Place of PublicationCalifornia, USA
PublisherAssociation for the Advancement of Artificial Intelligence
Pages3019-3026
Number of pages8
ISBN (Print)9781577358091
DOIs
Publication statusPublished - 23 Jul 2019
EventConference on Artificial Intelligence (33rd : 2019) - Hilton Hawaiian Village, Honolulu, United States
Duration: 27 Jan 20191 Feb 2019

Publication series

NameAAAI Conference on Artificial Intelligence
No.1
Volume33
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

ConferenceConference on Artificial Intelligence (33rd : 2019)
Abbreviated titleAAAI-19
CountryUnited States
CityHonolulu
Period27/01/191/02/19

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void ratio
logic
jettisoning
sentences
set theory
damage

Cite this

S. Ribeiro, J., Nayak, A., & Wassermann, R. (2019). Belief change and non-monotonic reasoning sans compactness. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence: AAAI 2019 (pp. 3019-3026). (AAAI Conference on Artificial Intelligence; Vol. 33, No. 1). California, USA: Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v33i01.33013019
S. Ribeiro, Jandson ; Nayak, Abhaya ; Wassermann, Renata. / Belief change and non-monotonic reasoning sans compactness. Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence: AAAI 2019. California, USA : Association for the Advancement of Artificial Intelligence, 2019. pp. 3019-3026 (AAAI Conference on Artificial Intelligence; 1).
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S. Ribeiro, J, Nayak, A & Wassermann, R 2019, Belief change and non-monotonic reasoning sans compactness. in Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence: AAAI 2019. AAAI Conference on Artificial Intelligence, no. 1, vol. 33, Association for the Advancement of Artificial Intelligence, California, USA, pp. 3019-3026, Conference on Artificial Intelligence (33rd : 2019), Honolulu, United States, 27/01/19. https://doi.org/10.1609/aaai.v33i01.33013019

Belief change and non-monotonic reasoning sans compactness. / S. Ribeiro, Jandson; Nayak, Abhaya; Wassermann, Renata.

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence: AAAI 2019. California, USA : Association for the Advancement of Artificial Intelligence, 2019. p. 3019-3026 (AAAI Conference on Artificial Intelligence; Vol. 33, No. 1).

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionResearchpeer-review

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S. Ribeiro J, Nayak A, Wassermann R. Belief change and non-monotonic reasoning sans compactness. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence: AAAI 2019. California, USA: Association for the Advancement of Artificial Intelligence. 2019. p. 3019-3026. (AAAI Conference on Artificial Intelligence; 1). https://doi.org/10.1609/aaai.v33i01.33013019