Abstract
The case-based reasoning community is successfully pursuing multiple approaches for applying deep learning methods to advance case-based reasoning. This “Challenges and Promises” paper argues for a complementary endeavor: pursuing ways that the case-based reasoning methodology can advance deep learning. Starting from challenges in deep learning and proposed neural-symbolic integrations based on specific technologies, it proposes studying how CBR ideas can inform choices of components for a new reasoning pipeline.
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Acknowledgment
This material is based upon work supported in part by the Department of the Navy, Office of Naval Research under award number N00014-19-1-2655. We thank the reviewers and Swaroop Vattam for helpful comments.
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Leake, D., Crandall, D. (2020). On Bringing Case-Based Reasoning Methodology to Deep Learning. In: Watson, I., Weber, R. (eds) Case-Based Reasoning Research and Development. ICCBR 2020. Lecture Notes in Computer Science(), vol 12311. Springer, Cham. https://doi.org/10.1007/978-3-030-58342-2_22
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