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Towards Automated Case Knowledge Discovery in the M 2 Case-Based Reasoning System

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Abstract

In this paper we present the M 2 Case-Based Reasoning (CBR) system. The M 2 system addresses a number of issues that present methodologies for CBR systems have shied away from. We discuss techniques for removing the knowledge acquisition bottleneck when acquiring case knowledge. Here, case knowledge refers to the complementary knowledge structures, cases (more specific in nature) and adaptation rules (more general). We address the use of negative cases for updating the case knowledge as well as for refining the similarity measures. In particular we discuss in detail, showing experimental results, the use of Data Mining within the M 2 system to build the case base from a database containing operational data, and discover adaptation rules. A methodology to monitor the competence of the CBR system and to utilize negative cases for updating the CBR system to enhance its competence is also discussed. The M 2 CBR system also employs Rough Set and Fuzzy Set theories to further enhance its capabilities within real-world applications as well as providing a richer and truer model of human reasoning.

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Authors and Affiliations

  1. Northern Ireland Knowledge Engineering Laboratory, University of Ulster, Northern Ireland

    D. Patterson, S. S. Anand, W. Dubitzky & J. G. Hughes

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  1. D. Patterson
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  2. S. S. Anand
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  3. W. Dubitzky
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  4. J. G. Hughes
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Correspondence to D. Patterson.

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Patterson, D., Anand, S.S., Dubitzky, W. et al. Towards Automated Case Knowledge Discovery in the M 2 Case-Based Reasoning System. Knowledge and Information Systems 1, 61–82 (1999). https://doi.org/10.1007/BF03325091

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  • DOI: https://doi.org/10.1007/BF03325091

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