Knowledge engineering is concerned with the development of qualitative computer models of processes, and procedures for applying these models to practical problems of diagnosis, design, control, etc. of complex systems. Knowledge acquisition research focuses on the development of computer tool for facilitating this modeling process, especially the accumulation of general representations and reasoning procedures that can be used for many problems. This work studies the foundational issues of formalization and human cognition that lie behind reuse of representation in new settings for new purposes. Situated cognition research suggests that people have capabilities for creating new representations that today's computers lack; furthermore, the meaning of a representation and the context that justifies it are always open to reinterpretation and can never be captured fully by representations. To deal with this, the work will represent the assumptions that justify changes to a model, so a knowledge acquisition program, but especially knowledge engineers themselves, will be able to intelligently reconstruct the origin and meaning of past representations, and hence more readily adapt them to new contexts. The main idea is to study how people use a layered history of cases, representations, and annotations to reinterpret and re-represent their past work as they discover and argue for changes to an evolving model. The result is improved failure-analysis techniques, coupled with a better understanding of what aspects of the knowledge engineering process cannot be automatized using current methods, but which appropriate tools can nevertheless facilitate.
