How we designed winning algorithms for abstract argumentation and which insight we attained
- Submitting institution
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The University of Huddersfield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 38
- Type
- D - Journal article
- DOI
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10.1016/j.artint.2019.08.001
- Title of journal
- Artificial Intelligence
- Article number
- -
- First page
- 1
- Volume
- 276
- Issue
- -
- ISSN
- 0004-3702
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2019
- URL
-
-
- 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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-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper builds on top of previous work published at TAFA 2013, COMMA 2014, KR 2016 and IJAIT 2017 that collectively have more than 140 citations (12/20). The presented system, called ArgSemSAT, won the PR track of the 2017 International Competition on Computational Models of Argumentation (http://argumentationcompetition.org/2017/results.html). This work showed, for the first time, how to efficiently and effectively solve complex argumentation-related problems. The presented system is freely available online (hhttps://github.com/federicocerutti/ArgSemSAT), is incorporated in the flagship repository website for argumentation benchmarking, and is used by the University of Dundee (http://www.arg-tech.org/AIFdb/argview/5695) to demonstrate state-of-the-art performance in the field.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -