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  1. No Access

    Chapter and Conference Paper

    Learning to refine indexing by introspective reasoning

    A significant problem for case-based reasoning (CBR) systems is determining the features to use in judging case similarity for retrieval. We describe research that addresses the feature selection problem by us...

    Susan Fox, David B. Leake in Case-Based Reasoning Research and Development (1995)

  2. No Access

    Chapter and Conference Paper

    Learning to improve case adaptation by introspective reasoning and CBR

    In current CBR systems, case adaptation is usually performed by rule-based methods that use task-specific rules hand-coded by the system developer. The ability to define those rules depends on knowledge of the...

    David B. Leake, Andrew Kinley in Case-Based Reasoning Research and Development (1995)

  3. No Access

    Book and Conference Proceedings

    Case-Based Reasoning Research and Development

    Second International Conference on Case-Based Reasoning, ICCBR-97 Providence, RI, USA, July 25–27, 1997 Proceedings

    David B. Leake, Enric Plaza in Lecture Notes in Computer Science (1997)

  4. No Access

    Chapter and Conference Paper

    A case study of case-based CBR

    Case-based reasoning depends on multiple knowledge sources beyond the case library, including knowledge about case adaptation and criteria for similarity assessment. Because hand coding this knowledge accounts...

    David B. Leake, Andrew Kinley in Case-Based Reasoning Research and Development (1997)

  5. No Access

    Chapter and Conference Paper

    Integrating Information Resources: A Case Study of Engineering Design Support⋆

    The development of successful case-based design aids depends both on the CBR processes themselves and on crucial questions of integrating the CBR system into the larger task context: how to make the CBR compon...

    David B. Leake, Larry Birnbaum in Case-Based Reasoning Research and Developm… (1999)

  6. No Access

    Chapter and Conference Paper

    When Experience is Wrong: Examining CBR for Changing Tasks and Environments⋆

    Case-based problem-solving systems reason and learn from experiences, building up case libraries of problems and solutions to guide future reasoning. The expected benefits of this learning process depend on tw...

    David B. Leake, David C. Wilson in Case-Based Reasoning Research and Development (1999)

  7. No Access

    Chapter and Conference Paper

    Combining CBR with Interactive Knowledge Acquisition, Manipulation and Reuse⋆

    Because of the complexity of aerospace design, intelligent systems to support and amplify the abilities of aerospace designers have the potential for profound impact on the speed and reliability of design gene...

    David B. Leake, David C. Wilson in Case-Based Reasoning Research and Development (1999)

  8. No Access

    Chapter and Conference Paper

    When Two Case Bases Are Better than One: Exploiting Multiple Case Bases

    Much current CBR research focuses on how to compact, refine, and augment the contents of individual case bases, in order to distill needed information into a single concise and authoritative source. However, a...

    David B. Leake, Raja Sooriamurthi in Case-Based Reasoning Research and Development (2001)

  9. No Access

    Chapter and Conference Paper

    Automatically Selecting Strategies for Multi-Case-Base Reasoning

    Case-based reasoning (CBR) systems solve new problems by retrieving stored prior cases, and adapting their solutions to fit new circumstances. Traditionally, CBR systems draw their cases from a single local ca...

    David B. Leake, Raja Sooriamurthi in Advances in Case-Based Reasoning (2002)

  10. Article

    Guest editors’ introduction: special issue on case-based reasoning

    David B. Leake, Barry Smyth, Rosina Weber in Journal of Intelligent Information Systems (2016)