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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.08264 |
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| _version_ | 1866913937992187904 |
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| author | Sood, Abhinav Grace, Kazjon Wan, Stephen Paris, Cecile |
| author_facet | Sood, Abhinav Grace, Kazjon Wan, Stephen Paris, Cecile |
| contents | Abductive reasoning, reasoning for inferring explanations for observations, is often mentioned in scientific, design-related and artistic contexts, but its understanding varies across these domains. This paper reviews how abductive reasoning is discussed in epistemology, science and design, and then analyses how various computational systems use abductive reasoning. Our analysis shows that neither theoretical accounts nor computational implementations of abductive reasoning adequately address generating creative hypotheses. Theoretical frameworks do not provide a straightforward model for generating creative abductive hypotheses, computational systems largely implement syllogistic forms of abductive reasoning. We break down abductive computational systems into components and conclude by identifying specific directions for future research that could advance the state of creative abductive reasoning in computational systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08264 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Abductive Computational Systems: Creative Abduction and Future Directions Sood, Abhinav Grace, Kazjon Wan, Stephen Paris, Cecile Artificial Intelligence Abductive reasoning, reasoning for inferring explanations for observations, is often mentioned in scientific, design-related and artistic contexts, but its understanding varies across these domains. This paper reviews how abductive reasoning is discussed in epistemology, science and design, and then analyses how various computational systems use abductive reasoning. Our analysis shows that neither theoretical accounts nor computational implementations of abductive reasoning adequately address generating creative hypotheses. Theoretical frameworks do not provide a straightforward model for generating creative abductive hypotheses, computational systems largely implement syllogistic forms of abductive reasoning. We break down abductive computational systems into components and conclude by identifying specific directions for future research that could advance the state of creative abductive reasoning in computational systems. |
| title | Abductive Computational Systems: Creative Abduction and Future Directions |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2507.08264 |