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SCIENCE CHINA Information Sciences, Volume 62, Issue 7: 076101(2019) https://doi.org/10.1007/s11432-018-9801-4

Abductive learning: towards bridging machine learning and logical reasoning

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  • ReceivedDec 21, 2018
  • AcceptedFeb 15, 2019
  • PublishedMar 8, 2019

Abstract

There is no abstract available for this article.


Acknowledgment

This work was supported by National Key RD Program of China (Grant No. 2018YFB1004300) and National Natural Science Foundation of China (Grant No. 61751306).


References

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  • Figure 1

    (a) Conventional supervised learning where the ground-truth labels of training data are given and (b) abductive learning where a classifier and a knowledge base are given. The given information is highlighted in black; the machine learning and logical reasoning components are shown in blue and green, respectively. In (b), the given classifier helps generate pseudo-labels, leading to pseudo-groundings; then, revisions to the pseudo groundings (shown as a red triangle) are generated via logical abduction based on minimizing the inconsistency with the knowledge base. The abduced labels are used to train the classifier, which is then adopted to replace the original classifier in the next iteration.

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