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On Bringing Case-Based Reasoning Methodology to Deep Learning

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Book cover Case-Based Reasoning Research and Development (ICCBR 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12311))

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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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Notes

  1. 1.

    https://iccbr2019.com/workshops/case-based-reasoning-and-deep-learning/.

  2. 2.

    Quoted from

    https://drive.google.com/file/d/1r-mDL4IX_hzZLDBKp8_e8VZqD7fOzBkF/view.

  3. 3.

    https://www.cs.rochester.edu/u/kautz/talks/Kautz%20Engelmore%20Lecture.pdf.

  4. 4.

    https://www.darpa.mil/program/data-driven-discovery-of-models.

References

  1. Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–52 (1994)

    Article  Google Scholar 

  2. Corchado, J.M., Lees, B.: Adaptation of cases for case based forecasting with neural network support. In: Pal, S.K., Dillon, T.S., Yeung, D.S. (eds.) Soft Computing in Case Based Reasoning. Springer, London (2001). https://doi.org/10.1007/978-1-4471-0687-6_13

  3. Grace, K., Maher, M.L., Wilson, D.C., Najjar, N.A.: Combining CBR and deep learning to generate surprising recipe designs. In: Goel, A., Díaz-Agudo, M.B., Roth-Berghofer, T. (eds.) ICCBR 2016. LNCS (LNAI), vol. 9969, pp. 154–169. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47096-2_11

    Chapter  Google Scholar 

  4. Hassan, H., et al.: Achieving human parity on automatic Chinese to English news translation (2018)

    Google Scholar 

  5. Kitano, H., Shimazu, H.: The experience sharing architecture: a case study in corporate-wide case-based software quality control. In: Leake, D. (ed.) Case-Based Reasoning: Experiences, Lessons, and Future Directions, pp. 235–268. AAAI Press, Menlo Park (1996)

    Google Scholar 

  6. Kolodner, J.: Retrieval and Organizational Strategies in Conceptual Memory. Lawrence Erlbaum, Hillsdale (1984)

    Google Scholar 

  7. Leake, D.: Assembling latent cases from the web: a challenge problem for cognitive CBR. In: Proceedings of the ICCBR-11 Workshop on Human-Centered and Cognitive Approaches to CBR (2011)

    Google Scholar 

  8. Liao, C., Liu, A., Chao, Y.: A machine learning approach to case adaptation. In: 2018 IEEE 1st International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), pp. 106–109 (2018)

    Google Scholar 

  9. de Mántaras, R.L., et al.: Retrieval, reuse, revision, and retention in CBR. Knowl. Eng. Rev. 20(3), 215–240 (2005)

    Article  Google Scholar 

  10. Mathisen, B.M., Aamodt, A., Bach, K., Langset̃h, H.: Learning similarity measures from data. Prog. Artif. Intell. 9, 129-143 (2019)

    Google Scholar 

  11. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR abs/1301.3781 (2013). http://arxiv.org/abs/1301.3781

  12. Lenz, M., Burkhard, H.-D., Bartsch-Spörl, B., Wess, S. (eds.): Case-Based Reasoning Technology. LNCS (LNAI), vol. 1400. Springer, Heidelberg (1998). https://doi.org/10.1007/3-540-69351-3

    Book  Google Scholar 

  13. Sani, S., Wiratunga, N., Massie, S.: Learning deep features for kNN-based human activity recognition. In: Proceedings of ICCBR 2017 Workshops (CAW, CBRDL, PO-CBR), Doctoral Consortium, and Competitions co-located with the 25th International Conference on Case-Based Reasoning (ICCBR 2017), Trondheim, Norway, June 26–28, 2017. CEUR Workshop Proceedings, vol. 2028, pp. 95–103. CEUR-WS.org (2017). http://ceur-ws.org/Vol-2028/paper9.pdf

  14. Silver, D., et al.: Mastering the game of go without human knowledge. Nature 550, 354–359 (2017)

    Article  Google Scholar 

  15. Taigman, Y., Yang, M., Ranzato, M.A., Wolf, L.: Deepface: closing the gap to human-level performance in face verification. In: IEEE Conference on Computer Vision and Pattern Recognition (2014)

    Google Scholar 

  16. Watson, I.: Case-based reasoning is a methodology not a technology. Knowl. Based Syst. 12, 303–308 (1996)

    Article  Google Scholar 

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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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Authors and Affiliations

  1. Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA

    David Leake & David Crandall

Authors
  1. David Leake
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  2. David Crandall
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Correspondence to David Leake .

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Editors and Affiliations

  1. School of Computer Science, University of Auckland, Auckland, New Zealand

    Ian Watson

  2. College of Computing and Informatics, Drexel University, Philadelphia, PA, USA

    Rosina Weber

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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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  • DOI: https://doi.org/10.1007/978-3-030-58342-2_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58341-5

  • Online ISBN: 978-3-030-58342-2

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