Realizing RCC8 networks using convex regions
- Submitting institution
-
Cardiff University / Prifysgol Caerdydd
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 97060053
- Type
- D - Journal article
- DOI
-
10.1016/j.artint.2014.10.002
- Title of journal
- Artificial Intelligence
- Article number
- -
- First page
- 74
- Volume
- 218
- Issue
- -
- ISSN
- 0004-3702
- Open access status
- Out of scope for open access requirements
- Month of publication
- October
- Year of publication
- 2014
- URL
-
http://dx.doi.org/10.1016/j.artint.2014.10.002
- 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
-
1
- Research group(s)
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A - Artificial intelligence and data analytics
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The central vision of this work is that formalising commonsense reasoning requires a framework for symbolic reasoning about cognitive models. Such models often represent meaning using convex regions. We show that the well-known RCC8 calculus can be faithfully used to reason about such representations. This opens up substantial new applications for RCC8, and for this reason, an earlier conference version of this work (http://dx.doi.org/10.3233/978-1-61499-098-7-726) was nominated as the runner-up best paper award at the European Conference on Artificial Intelligence. This work also provided a central justification underpinning the ERC Starting Grant of the first author (€1.45 million: https://cordis.europa.eu/project/id/637277).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -