Modeling and Retrieval of Context
Second International Workshop, MRC 2005, Edinburgh, UK, July 31–August 1, 2005, Revised Selected Papers
Article
Book and Conference Proceedings
Second International Workshop, MRC 2005, Edinburgh, UK, July 31–August 1, 2005, Revised Selected Papers
Chapter and Conference Paper
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...
Chapter and Conference Paper
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...
Article
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...
Chapter and Conference Paper
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 ...
Chapter and Conference Paper
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...
Chapter and Conference Paper
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...
Chapter and Conference Paper
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...
Chapter and Conference Paper
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...
Chapter and Conference Paper
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...
Chapter and Conference Paper
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...
Book and Conference Proceedings
Second International Conference on Case-Based Reasoning, ICCBR-97 Providence, RI, USA, July 25–27, 1997 Proceedings
Chapter and Conference Paper
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...
Article
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...
Chapter and Conference Paper
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...
Chapter and Conference Paper
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...