A collaborative engineering team can be viewed as a collection of human engineers and intelligent computer systems, called intelligent agents. The process of collaborative engineering involves application of knowledge by engineers and intelligent agents, transmitting reports and commands between the engineers, and exchange of information between relevant parties. As engineering problems become more complex and time-to-market more demanding, new technologies must be developed to support knowledge application, decision-making and control, and information exchange. Most of the current research on collaborative engineering support focuses on providing communication and data sharing support for effective coordination. We argue that in order to increase the productivity of collaborative engineering, we need mechanisms that can provide active knowledge level support for engineers. Our research on K1CAD — a Knowledge Infrastructure for Collaborative and Agent-based Design — attempts to develop a network of intelligent agents that capture knowledge from their associated human engineers and provide knowledge level support to them when needed. One important issue involved in developing such a framework is how can we define and assess the role of knowledge and how different ways of organizing knowledge may impact on the overall performance of a collaborative engineering team? In this paper, we introduce the notion of knowledge structure, and present our initial model of knowledge structure that identifies the roles of knowledge and provides measures to assess how knowledge structure may impact on team performance. An example is presented to illustrate some interesting features of the model.

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