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@@ -10,6 +10,9 @@ |
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# ABLkit: A Toolkit for Abductive Learning |
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- [Documentation](https://ablkit.readthedocs.io/en/latest/index.html) |
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- [Read the paper](https://journal.hep.com.cn/fcs/EN/10.1007/s11704-024-40085-7) |
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**ABLkit** is an efficient Python toolkit for [**Abductive Learning (ABL)**](https://www.lamda.nju.edu.cn/publication/chap_ABL.pdf). ABL is a novel paradigm that integrates machine learning and logical reasoning in a unified framework. It is suitable for tasks where both data and (logical) domain knowledge are available. |
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<p align="center"> |
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@@ -182,7 +185,7 @@ bridge.test(test_data) |
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</details> |
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To explore detailed tutorials and information, please refer to - [document](https://ablkit.readthedocs.io/en/latest/index.html). |
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To explore detailed tutorials and information, please refer to: [Documentation on Read the Docs](https://ablkit.readthedocs.io/en/latest/index.html). |
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## Examples |
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@@ -218,4 +221,20 @@ For more information about ABL, please refer to: [Zhou, 2019](http://scis.scichi |
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address = {Amsterdam}, |
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year = {2022} |
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} |
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``` |
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## Cite |
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To cite ABLkit, please cite the following paper: [Huang et al., 2024](https://journal.hep.com.cn/fcs/EN/10.1007/s11704-024-40085-7). |
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``` |
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@article{ABLkit2024, |
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author = {Huang, Yu-Xuan and Hu, Wen-Chao and Gao, En-Hao and Jiang, Yuan}, |
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title = {ABLkit: a Python toolkit for abductive learning}, |
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journal = {Frontiers of Computer Science}, |
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volume = {18}, |
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number = {6}, |
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pages = {186354}, |
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year = {2024} |
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} |
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``` |