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A Critical Review of Inductive Logic Programming Techniques for Explainable AI. (arXiv:2112.15319v1 [cs.LG])
Jan. 3, 2022, 2:10 a.m. | Zheng Zhang, Levent Yilmaz, Bo Liu
cs.LG updates on arXiv.org arxiv.org
Despite recent advances in modern machine learning algorithms, the opaqueness
of their underlying mechanisms continues to be an obstacle in adoption. To
instill confidence and trust in artificial intelligence systems, Explainable
Artificial Intelligence has emerged as a response to improving modern machine
learning algorithms' explainability. Inductive Logic Programming (ILP), a
subfield of symbolic artificial intelligence, plays a promising role in
generating interpretable explanations because of its intuitive logic-driven
framework. ILP effectively leverages abductive reasoning to generate
explainable first-order clausal theories …
More from arxiv.org / cs.LG updates on arXiv.org
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