Case-Based Reasoning Research and Development
Second International Conference on Case-Based Reasoning, ICCBR-97 Providence, RI, USA, July 25–27, 1997 Proceedings
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David B. Leake
Computer Science
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Article
Guest editors’ introduction: special issue on case-based reasoning
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Chapter and Conference Paper
Human-Centered CBR: Integrating Case-Based Reasoning with Knowledge Construction and Extension
Human-centered computing studies methods to improve the interactions and performance of combined human/machine systems. Case-based reasoning provides a natural method for supporting knowledge capture and acces...
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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...
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Article
A Case-Based Framework for Interactive Capture and Reuse of Design Knowledge
Aerospace design is a complex task requiring access to large amounts of specialized information. Consequently, intelligent systems that support and amplify the abilities of human designers by capturing and pre...
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Chapter and Conference Paper
Word Sieve: A Method for Real-Time Context Extraction
In order to be useful, intelligent information retrieval agents must provide their users with context-relevant information. This paper presents WordSieve, an algorithm for automatically extracting information ...
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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...
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Chapter and Conference Paper
Remembering Why to Remember: Performance-Guided Case-Base Maintenance
An important focus of recent CBR research is on how to develop strategies for achieving compact, competent case-bases, as a way to improve the performance of CBR systems. However, compactness and competence ar...
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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...
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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...
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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...
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Chapter and Conference Paper
Categorizing case-base maintenance: Dimensions and directions
Experience with the growing number of large-scale CBR systems has led to increasing recognition of the importance of case-base maintenance. Multiple researchers have addressed pieces of the case-base maintenan...
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Book and Conference Proceedings
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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...
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Article
Learning, goals, and learning goals: A perspective on goal-driven learning
In cognitive science, artificial intelligence, psychology, and education, a growing body of research supports the view that the learning process is strongly influenced by the learner's goals. The fundamental t...
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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...
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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...
