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Learning and Applying Adaptation Operators in Process-Oriented Case-Based Reasoning

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Case-Based Reasoning Research and Development (ICCBR 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9343))

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Abstract

This paper presents a novel approach to the operator-based adaptation of workflows, which is a specific type of transformational adaptation. We introduce the notion of workflow adaptation operators which are partial functions transforming a workflow into a successor workflow, specified by workflow fractions to be inserted and/or deleted. The adaptation process itself chains adaptation operators during a local search process aiming at fulfilling the query as best as possible. Further, the paper presents an algorithm that learns workflow adaptation operators from the case base automatically, thereby addressing the common problem of adaptation knowledge acquisition. An empirical evaluation in the domain of cooking workflows was conducted which demonstrates convincing adaptation capabilities without a significant reduction of the workflows’ quality.

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Notes

  1. 1.

    The change request only contained ingredients and preparation steps present in the workflows from the case base and no ingredients that are used as mixtures of multiple ingredients (e.g., vegetable mix).

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Acknowledgements

This work was funded by the German Research Foundation (DFG), project number BE 1373/3-1.

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Authors and Affiliations

  1. Business Information Systems II, University of Trier, 54286, Trier, Germany

    Gilbert Müller & Ralph Bergmann

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  1. Gilbert Müller
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  2. Ralph Bergmann
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Correspondence to Gilbert Müller .

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Editors and Affiliations

  1. Universität Paderborn, Paderborn, Germany

    Eyke Hüllermeier

  2. Goethe-Universität Frankfurt, Frankfurt/Main, Germany

    Mirjam Minor

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Müller, G., Bergmann, R. (2015). Learning and Applying Adaptation Operators in Process-Oriented Case-Based Reasoning. In: Hüllermeier, E., Minor, M. (eds) Case-Based Reasoning Research and Development. ICCBR 2015. Lecture Notes in Computer Science(), vol 9343. Springer, Cham. https://doi.org/10.1007/978-3-319-24586-7_18

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  • DOI: https://doi.org/10.1007/978-3-319-24586-7_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24585-0

  • Online ISBN: 978-3-319-24586-7

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