Reference: Gruber, T. R.; Baudin, C.; Boose, J.; & Weber, J. Design Rationale Capture as Knowledge Acquisition: Tradeoffs in the Design of Interactive Tools in Machine Learning. Morgan Kaufmann, San Mateo, CA, 1991.
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.
Full paper available as hqx, ps.