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A Method for Predicting Solutions in Case-Based Problem Solving

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Advances in Case-Based Reasoning (EWCBR 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1898))

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Abstract

In order to predict the solution to a new problem we proceed from the “similar problem-similar solution” assumption underlying case-based reasoning. The concept of a similarity hypothesis is introduced as a formal model of this meta-heuristic. It allows for realizing a constraint-based inference scheme which derives a prediction in the form of a set of possible candidates. We propose an algorithm for learning a suitable similarity hypothesis from a sequence of observations. Basing the inference process on hypotheses thus defined yields (set-valued) predictions that cover the true solution with high probability. Our method is meant to support the overall (case-based) problem solving process by bringing a promising set of possible solutions into focus.

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Author information

Authors and Affiliations

  1. Institut de Recherche en Informatique de Toulouse, Université Paul Sabatier, France

    Eyke Hüllermeier

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  1. Eyke Hüllermeier
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Editors and Affiliations

  1. Centre for Scientific and Technological Research (ITC-irst), Istituto Trention di Cultura, via Sommarive 18, 38050, Povo (Trento), Italy

    Enrico Blanzieri

  2. DISTA, University of Eastern Piedmont “Amedeo Avogadro”, C.so Borsalino 54, 15100, Alessandria, Italy

    Luigi Portinale

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Hüllermeier, E. (2000). A Method for Predicting Solutions in Case-Based Problem Solving. In: Blanzieri, E., Portinale, L. (eds) Advances in Case-Based Reasoning. EWCBR 2000. Lecture Notes in Computer Science, vol 1898. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44527-7_12

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  • DOI: https://doi.org/10.1007/3-540-44527-7_12

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67933-2

  • Online ISBN: 978-3-540-44527-2

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