Design rationale capture as knowledge acquisition: tradeoffs in the design of interactive tools in machine learning

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abstract: This paper introduces a panel to be held at the Knowledge Acquisition Track ofthe Machine Learning Workshop (ML91). This panel will focus on the problem ofacquiring design rationale knowledge from humans for later reuse. The designof tools for design rationale capture reveals several fundamental issues forknowledge acquisition, such as the relationships among formality andexpressiveness of representations, and kinds of automated support forelicitation and analysis of knowledge. This paper sets the background fordiscussion by identifying dimensions of a design space for design rationaletools, and then includes position statements from each panelist arguing forvarious positions in this space.

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