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One area of active research is on identification of artifacts.  To support the expert system development, the CITI-AI is conducting research in how humans make decisions.

The following articles are in Knowledge Elicitation

Knowledge Elicitation

Expert systems are computer programs which are intended to solve real-world problems, achieving the same level of accuracy as human experts. There are many obstacles in such an endeavour. One of the greatest is the acquisition of the knowledge which human expert use in their problem solving. The issue is so important to the development of knowledge based systems  that it has been described as the 'bottle-neck for  Expert Systems construction' (Hayes-Roth et al., 1983). Despite its central role there is no comprehensive theory of knowledge acquisition available. Many regard the area as an art rather than a science. It is not the purpose of this chapter to investigate the theoretical shortcomings of  knowledge acquisition but to deliver practical advice and guidance on performing the process.

Development of a methodology for optimizing elicited knowledge

In this paper a conceptual framework and an operational methodology is presented for describing the most appropriate knowledge eliciting method

Eliciting Knowledge from Experts: A Methodological Analysis

The psychological study of expertise has a rich background and has recently gained impetus in part because of the advent of expert systems and related technologies for preserving knowledge. In the study of expertise,
whether in the context of applications or the context of psychological research, knowledge elicitation is a crucial step. Research in a number of traditions -judgment and decision making, human factors, cognitive science, expert systems-has utilized a variety of knowledge elicitation methods.

Effective Knowledge Management in Knowledge-Intensive Organizations

This paper outlines an approach to determine the effectiveness of knowledge management (KM) in knowledge intensive organizations. ‘Effectiveness’ implies embedding KM processes in an organizational context. We introduce the Knowledge Governance Framework that includes knowledge resources, knowledge development, three types of KM, and organizational objectives. We applied the framework in two case studies to identify the three types of KM (operational KM, maintenance KM, and long-term KM), to determine what knowledge-intensive organizations regard to be effective KM and how they measure the effectiveness. Both cases indicate relations between ‘use and development of knowledge resources’ and ‘business objectives’, but the relations are managed only on a limited scale and on an ad-hoc basis. We found that KM objectives can be qualitative, implicit, and emergent (case one) as well as explicit (the use of business cases for portal investments; case two). We conclude with two hypothesis to be tested in further research.