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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2507.04994 |
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| _version_ | 1866911042487975936 |
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| author | Gould, Adam Gaul, Gabriel de Olim Toni, Francesca |
| author_facet | Gould, Adam Gaul, Gabriel de Olim Toni, Francesca |
| contents | We introduce Supported Abstract Argumentation for Case-Based Reasoning (sAA-CBR), a binary classification model in which past cases engage in debates by arguing in favour of their labelling and attacking or supporting those with opposing or agreeing labels. With supports, sAA-CBR overcomes the limitation of its precursor AA-CBR, which can contain extraneous cases (or spikes) that are not included in the debates. We prove that sAA-CBR contains no spikes, without trading off key model properties |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04994 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Supported Abstract Argumentation for Case-Based Reasoning Gould, Adam Gaul, Gabriel de Olim Toni, Francesca Artificial Intelligence We introduce Supported Abstract Argumentation for Case-Based Reasoning (sAA-CBR), a binary classification model in which past cases engage in debates by arguing in favour of their labelling and attacking or supporting those with opposing or agreeing labels. With supports, sAA-CBR overcomes the limitation of its precursor AA-CBR, which can contain extraneous cases (or spikes) that are not included in the debates. We prove that sAA-CBR contains no spikes, without trading off key model properties |
| title | Supported Abstract Argumentation for Case-Based Reasoning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2507.04994 |