Design Rationale Capture as Knowledge Acquisition: Tradeoffs in the Design of Interactive Tools in Machine Learning

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Citation: Thomas R. Gruber and Catherine Baudin and John H. Boose and Jay C. Weber. (1991) Design Rationale Capture as Knowledge Acquisition: Tradeoffs in the Design of Interactive Tools in Machine Learning. In Machine Learning: Proceedings of the Eighth International Workshop, 1991.

Publication inproceedings ( Edit )
type InProceedings
bibtype inproceedings
Bibtex basics
author Thomas R. Gruber and Catherine Baudin and John H. Boose and Jay C. Weber
title Design Rationale Capture as Knowledge Acquisition: Tradeoffs in the Design of Interactive Tools in Machine Learning
booktitle Machine Learning: Proceedings of the Eighth International Workshop
address San Mateo, CA
year 1991
Bibtex more
publisher Morgan Kaufmann
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abstract This paper introduces a panel to be held at the Knowledge Acquisition Track of the Machine Learning Workshop (ML91). This panel will focus on the problem of acquiring design rationale knowledge from humans for later reuse. The design of tools for design rationale capture reveals several fundamental issues for knowledge acquisition, such as the relationships among formality and expressiveness of representations, and kinds of automated support for elicitation and analysis of knowledge. This paper sets the background for discussion by identifying dimensions of a design space for design rationale tools, and then includes position statements from each panelist arguing for various positions in this space.

KSL Technical Report ID: KSL-91-47
Facts about Design Rationale Capture as Knowledge Acquisition: Tradeoffs in the Design of Interactive Tools in Machine LearningRDF feed
Abstract This paper introduces a panel to be held a … This paper introduces a panel to be held at the Knowledge Acquisition Track of the Machine Learning Workshop (ML91). This panel will focus on the problem of acquiring design rationale knowledge from humans for later reuse. The design of tools for design rationale capture reveals several fundamental issues for knowledge acquisition, such as the relationships among formality and expressiveness of representations, and kinds of automated support for elicitation and analysis of knowledge. This paper sets the background for discussion by identifying dimensions of a design space for design rationale tools, and then includes position statements from each panelist arguing for various positions in this space. guing for various positions in this space.
Address San Mateo, CA  +
Author Thomas R. Gruber and Catherine Baudin and John H. Boose and Jay C. Weber  +
Bibtype inproceedings  +
Booktitle Machine Learning: Proceedings of the Eighth International Workshop  +
Has author Thomas R. Gruber and Catherine Baudin and John H. Boose and Jay C. Weber  +
Has identifier KSL-91-47  +
Has publishing details 1991  +
Has title Design Rationale Capture as Knowledge Acquisition: Tradeoffs in the Design of Interactive Tools in Machine Learning  +
Has where published Machine Learning: Proceedings of the Eighth International Workshop  +
Has year 1991  +
Ksl tr id KSL-91-47  +
Process note GOOGLE  +
Publisher Morgan Kaufmann  +
Title Design Rationale Capture as Knowledge Acquisition: Tradeoffs in the Design of Interactive Tools in Machine Learning  +
Year 1991  +
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