Know your exceptions: Towards an Ontology of Exceptions in Knowledge Representation
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911789497712640 |
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| author | Sacco, Gabriele Bozzato, Loris Kutz, Oliver |
| author_facet | Sacco, Gabriele Bozzato, Loris Kutz, Oliver |
| contents | Defeasible reasoning is a kind of reasoning where some generalisations may not be valid in all circumstances, that is general conclusions may fail in some cases. Various formalisms have been developed to model this kind of reasoning, which is characteristic of common-sense contexts. However, it is not easy for a modeller to choose among these systems the one that better fits its domain from an ontological point of view. In this paper we first propose a framework based on the notions of exceptionality and defeasibility in order to be able to compare formalisms and reveal their ontological commitments. Then, we apply this framework to compare four systems, showing the differences that may occur from an ontological perspective. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00685 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Know your exceptions: Towards an Ontology of Exceptions in Knowledge Representation Sacco, Gabriele Bozzato, Loris Kutz, Oliver Artificial Intelligence I.2.4 Defeasible reasoning is a kind of reasoning where some generalisations may not be valid in all circumstances, that is general conclusions may fail in some cases. Various formalisms have been developed to model this kind of reasoning, which is characteristic of common-sense contexts. However, it is not easy for a modeller to choose among these systems the one that better fits its domain from an ontological point of view. In this paper we first propose a framework based on the notions of exceptionality and defeasibility in order to be able to compare formalisms and reveal their ontological commitments. Then, we apply this framework to compare four systems, showing the differences that may occur from an ontological perspective. |
| title | Know your exceptions: Towards an Ontology of Exceptions in Knowledge Representation |
| topic | Artificial Intelligence I.2.4 |
| url | https://arxiv.org/abs/2403.00685 |