Epistemic graphs for representing and reasoning with positive and negative influences of arguments
- Submitting institution
-
Cardiff University / Prifysgol Caerdydd
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 101621529
- Type
- D - Journal article
- DOI
-
10.1016/j.artint.2020.103236
- Title of journal
- Artificial Intelligence
- Article number
- 103236
- First page
- -
- Volume
- 281
- Issue
- -
- ISSN
- 0004-3702
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2020
- URL
-
http://dx.doi.org/10.1016/j.artint.2020.103236
- 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
-
2
- Research group(s)
-
A - Artificial intelligence and data analytics
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes a novel formalism for argumentation, called epistemic graphs, introduced to address shortcomings of existing models highlighted by experiments with human participants (https://doi.org/10.1016/j.ijar.2017.11.009). The paper provides a deep study of the properties of epistemic graphs, showing how they subsume other important argumentation models, and provides a sound and complete proof system. The work led to further research on epistemic graphs that includes update methods (e.g., https://doi.org/10.1007/978-3-030-29765-7_7), applications in persuasion (e.g., https://doi.org/10.1016/j.ijar.2019.07.006), and proof-of-concept implementations (e.g., https://doi.org/10.1007/978-3-030-29765-7_5).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -