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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
- 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.
- In this paper a conceptual framework and an operational methodology is
presented for describing the most appropriate knowledge eliciting method
- 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.
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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.
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Knowledge management is becoming the critical source of
competitive advantage. Effective knowledge management differentiates leading
companies from their less successful counterparts. The way organisations use
knowledge and learn is increasingly being recognised as central to their
effectiveness. Construction is no exception. Many construction companies,
and their clients, realise that the way they manage knowledge and learn over
time and across the whole supply chain can make a significant difference to
their performance and to the efficiency of the construction process. This
paper describes work forming part of a project entitled Knowledge & Learning
In CONstruction (KLICON). The paper concentrates on the knowledge management
issues at the interface between designers and contractors in construction
projects. The paper also investigates the role of IT within knowledge
management in designer/constructor interface.
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When we think of how knowledge will be managed in 10 years,
all sorts of futuristic images come to mind. There will be Superfast
Ethernet jacks on every knowledge worker's neck allowing knowledge
downloads. Our heads will have swelled in "Brainiac" fashion to contain the
increased knowledge of the new millennium. Intelligent agents (why are there
never stupid agents?) will cruise the InterInterNet, seeking out juicy bits
of knowledge wherever they hide. Pocket-sized "knowledge appliances" will
pull relevant knowledge from wireless networks and display it on our
glasses.
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