Eliciting Rational Initial Weights in Gradual Argumentation
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915146057646080 |
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| author | Oren, Nir Yun, Bruno |
| author_facet | Oren, Nir Yun, Bruno |
| contents | Many semantics for weighted argumentation frameworks assume that each argument is associated with an initial weight. However, eliciting these initial weights poses challenges: (1) accurately providing a specific numerical value is often difficult, and (2) individuals frequently confuse initial weights with acceptability degrees in the presence of other arguments. To address these issues, we propose an elicitation pipeline that allows one to specify acceptability degree intervals for each argument. By employing gradual semantics, we can refine these intervals when they are rational, restore rationality when they are not, and ultimately identify possible initial weights for each argument. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_07452 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Eliciting Rational Initial Weights in Gradual Argumentation Oren, Nir Yun, Bruno Artificial Intelligence Many semantics for weighted argumentation frameworks assume that each argument is associated with an initial weight. However, eliciting these initial weights poses challenges: (1) accurately providing a specific numerical value is often difficult, and (2) individuals frequently confuse initial weights with acceptability degrees in the presence of other arguments. To address these issues, we propose an elicitation pipeline that allows one to specify acceptability degree intervals for each argument. By employing gradual semantics, we can refine these intervals when they are rational, restore rationality when they are not, and ultimately identify possible initial weights for each argument. |
| title | Eliciting Rational Initial Weights in Gradual Argumentation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2502.07452 |