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Explanatory machine learning for sequential human teaching. (arXiv:2205.10250v1 [cs.AI])
May 23, 2022, 1:11 a.m. | Lun Ai, Johannes Langer, Stephen H. Muggleton, Ute Schmid
cs.LG updates on arXiv.org arxiv.org
The topic of comprehensibility of machine-learned theories has recently drawn
increasing attention. Inductive Logic Programming (ILP) uses logic programming
to derive logic theories from small data based on abduction and induction
techniques. Learned theories are represented in the form of rules as
declarative descriptions of obtained knowledge. In earlier work, the authors
provided the first evidence of a measurable increase in human comprehension
based on machine-learned logic rules for simple classification tasks. In a
later study, it was found that …
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