Generalized possibilistic logic: foundations and applications to qualitative reasoning about uncertainty
- Submitting institution
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Cardiff University / Prifysgol Caerdydd
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 97062159
- Type
- D - Journal article
- DOI
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10.1016/j.artint.2017.08.001
- Title of journal
- Artificial Intelligence
- Article number
- -
- First page
- 139
- Volume
- 252
- Issue
- -
- ISSN
- 0004-3702
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2017
- URL
-
https://doi.org/10.1016/j.artint.2017.08.001
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
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A - Artificial intelligence and data analytics
- Citation count
- 11
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Possibilistic logic, developed by the first two authors of this paper, provides the theoretical basis for the theory of fuzzy logic. It has a long tradition going back to the 1980s and serves as a unifying framework for important Knowledge Representation frameworks, such as AGM belief revision and default reasoning. The proposed generalisation allows us to unify for the first time the two main traditions of non-monotonic reasoning in AI. The paper extends work that was published in the proceedings of KR 2012 and ECAI 2014. This work is partly funded by an ERC Starting Grant (€1.45 million: https://cordis.europa.eu/project/id/637277).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -