A Smooth Transition Between Induction and Deduction: Fast Abductive Learning Based on Probabilistic Symbol Perception Article Swipe
Lin-Han Jia
,
Siyu Han
,
Lan-Zhe Guo
,
Zhi Zhou
,
Zhaolong Li
,
Yu-Feng Li
,
Zhi‐Hua Zhou
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2502.12919
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2502.12919
Abductive learning (ABL) that integrates strengths of machine learning and logical reasoning to improve the learning generalization, has been recently shown effective. However, its efficiency is affected by the transition between numerical induction and symbolical deduction, leading to high computational costs in the worst-case scenario. Efforts on this issue remain to be limited. In this paper, we identified three reasons why previous optimization algorithms for ABL were not effective: insufficient utilization of prediction, symbol relationships, and accumulated experience in successful abductive processes, resulting in redundant calculations to the knowledge base. To address these challenges, we introduce an optimization algorithm named as Probabilistic Symbol Perception (PSP), which makes a smooth transition between induction and deduction and keeps the correctness of ABL unchanged. We leverage probability as a bridge and present an efficient data structure, achieving the transfer from a continuous probability sequence to discrete Boolean sequences with low computational complexity. Experiments demonstrate the promising results.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2502.12919
- https://arxiv.org/pdf/2502.12919
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407759874
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4407759874Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2502.12919Digital Object Identifier
- Title
-
A Smooth Transition Between Induction and Deduction: Fast Abductive Learning Based on Probabilistic Symbol PerceptionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-02-18Full publication date if available
- Authors
-
Lin-Han Jia, Siyu Han, Lan-Zhe Guo, Zhi Zhou, Zhaolong Li, Yu-Feng Li, Zhi‐Hua ZhouList of authors in order
- Landing page
-
https://arxiv.org/abs/2502.12919Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2502.12919Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2502.12919Direct OA link when available
- Concepts
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Symbol (formal), Probabilistic logic, Transition (genetics), Perception, Abductive reasoning, Computer science, Mathematics, Artificial intelligence, Psychology, Chemistry, Biochemistry, Programming language, Neuroscience, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.probability | 123, 139 |
| abstract_inverted_index.utilization | 70 |
| abstract_inverted_index.calculations | 85 |
| abstract_inverted_index.insufficient | 69 |
| abstract_inverted_index.optimization | 62, 97 |
| abstract_inverted_index.Probabilistic | 101 |
| abstract_inverted_index.computational | 39, 147 |
| abstract_inverted_index.relationships, | 74 |
| abstract_inverted_index.generalization, | 16 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |