Ambiguity-Aware Abductive Learning

Hao-Yuan He, Hui Sun, Zheng Xie, Ming Li
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:18019-18042, 2024.

Abstract

Abductive Learning (ABL) is a promising framework for integrating sub-symbolic perception and logical reasoning through abduction. In this case, the abduction process provides supervision for the perception model from the background knowledge. Nevertheless, this process naturally contains uncertainty, since the knowledge base may be satisfied by numerous potential candidates. This implies that the result of the abduction process, i.e., a set of candidates, is ambiguous; both correct and incorrect candidates are mixed in this set. The prior art of abductive learning selects the candidate that has the minimal inconsistency of the knowledge base. However, this method overlooks the ambiguity in the abduction process and is prone to error when it fails to identify the correct candidates. To address this, we propose Ambiguity-Aware Abductive Learning ($\textrm{A}^3\textrm{BL}$), which evaluates all potential candidates and their probabilities, thus preventing the model from falling into sub-optimal solutions. Both experimental results and theoretical analyses prove that $\textrm{A}^3\textrm{BL}$ markedly enhances ABL by efficiently exploiting the ambiguous abduced supervision.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-he24j, title = {Ambiguity-Aware Abductive Learning}, author = {He, Hao-Yuan and Sun, Hui and Xie, Zheng and Li, Ming}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {18019--18042}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/he24j/he24j.pdf}, url = {https://proceedings.mlr.press/v235/he24j.html}, abstract = {Abductive Learning (ABL) is a promising framework for integrating sub-symbolic perception and logical reasoning through abduction. In this case, the abduction process provides supervision for the perception model from the background knowledge. Nevertheless, this process naturally contains uncertainty, since the knowledge base may be satisfied by numerous potential candidates. This implies that the result of the abduction process, i.e., a set of candidates, is ambiguous; both correct and incorrect candidates are mixed in this set. The prior art of abductive learning selects the candidate that has the minimal inconsistency of the knowledge base. However, this method overlooks the ambiguity in the abduction process and is prone to error when it fails to identify the correct candidates. To address this, we propose Ambiguity-Aware Abductive Learning ($\textrm{A}^3\textrm{BL}$), which evaluates all potential candidates and their probabilities, thus preventing the model from falling into sub-optimal solutions. Both experimental results and theoretical analyses prove that $\textrm{A}^3\textrm{BL}$ markedly enhances ABL by efficiently exploiting the ambiguous abduced supervision.} }
Endnote
%0 Conference Paper %T Ambiguity-Aware Abductive Learning %A Hao-Yuan He %A Hui Sun %A Zheng Xie %A Ming Li %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-he24j %I PMLR %P 18019--18042 %U https://proceedings.mlr.press/v235/he24j.html %V 235 %X Abductive Learning (ABL) is a promising framework for integrating sub-symbolic perception and logical reasoning through abduction. In this case, the abduction process provides supervision for the perception model from the background knowledge. Nevertheless, this process naturally contains uncertainty, since the knowledge base may be satisfied by numerous potential candidates. This implies that the result of the abduction process, i.e., a set of candidates, is ambiguous; both correct and incorrect candidates are mixed in this set. The prior art of abductive learning selects the candidate that has the minimal inconsistency of the knowledge base. However, this method overlooks the ambiguity in the abduction process and is prone to error when it fails to identify the correct candidates. To address this, we propose Ambiguity-Aware Abductive Learning ($\textrm{A}^3\textrm{BL}$), which evaluates all potential candidates and their probabilities, thus preventing the model from falling into sub-optimal solutions. Both experimental results and theoretical analyses prove that $\textrm{A}^3\textrm{BL}$ markedly enhances ABL by efficiently exploiting the ambiguous abduced supervision.
APA
He, H., Sun, H., Xie, Z. & Li, M.. (2024). Ambiguity-Aware Abductive Learning. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:18019-18042 Available from https://proceedings.mlr.press/v235/he24j.html.

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