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6. FUTURE TRENDS AND SUMMARY

Knowledge acquisition has been described as a very difficult and time-consuming task that frequently creates a bottleneck in an expert system development effort. There is no single way to avoid the difficulties of knowledge acquisition. However, by identifying the right domain experts, and employing a combination of proper techniques and a structured methodology, we believe that the knowledge acquisition task can be performed more easily as well as more effectively and efficiently than it used to be.

As expert systems become more and more complicated and the problem domains become more complex, the required expertise often resides in not one but a group of experts. Moreover, interaction among experts creates a synergy that results in an enriched and enlarged domain of expertise. These factors all point in the direction of increased use of multiple experts in the expert system development project whenever possible. There is no single knowledge acquisition technique that is the best and most effective; the trend is toward using a combination of techniques that best fit the domain of expertise.

The work of automating the knowledge acquisition process is in progress, but most existing tools have been designed for eliciting knowledge from individuals. The ETS and AQUINAS systems developed by Boose (1989) try to address situations where multiple experts are involved, but they are limited to certain applications. It should be noted that most tools are still in the research and development stage and are available only for in-house use. Nevertheless, the progress in automation of knowledge acquisition is on-going. To be most useful, the focus of this research should be on acquiring the kinds of knowledge that are difficult to acquire manually but for which automated methods are feasible. Research in neural networks and case-based reasoning for knowledge acquisition is another important trend in knowledge acquisition.

The knowledge acquisition methodology described in this chapter was tested in an information center domain where multiple experts were involved in the development of a help service expert system. Positive results indicated the importance of having a methodology and showed the applicability of such a methodology to be used in a group environment. Further efforts to generalize this methodology to test its applicability in other application domains and to incorporate various knowledge acquisition techniques are needed.

The term "knowledge acquisition" usually refers to the acquisition of knowledge for building expert systems. However, acquiring knowledge from a group of people is a ubiquitous activity that can be found in many group tasks such as strategic planning, system design, negotiation, and decision making. For instance, major software design decisions are usually made in face-to-face software design meetings involving users, managers, and system developers. Users have knowledge of how current systems work and requirements of the new systems. Managers know the strategic implications of the new system. System developers can intrigue users and managers by providing them with knowledge of how advanced information technologies can serve them. We may examine many other group works from the knowledge acquisition perspective.

REFERENCES

  1. Boose, J.H. (1989). "Using repertory grid-centered knowledge acquisition tools for decision support,"Proceedings of the Twenty-Second Annual Hawaii International Conference on System Sciences, Vol. III, Kona, HI, January 3-6, 211-220.
  2. Cox, Leon D., Al-Ghanim, Amjed M., Culler, David E. (1995). "A neural network-based methodology for machining knowledge acquisition," Computers & Industrial Engineering, (29:1-4), September, 217-220.
  3. Kelly, G. (1955). The Psychology of Personal Constructs, Norton, New York.
  4. Liou, Yihwa Irene (1992). "Collaborative knowledge acquisition," Expert Systems with Applications: An International Journal, 5:1-13.
  5. Liou, Y.I., Weber, E.S., and Nunamaker, J.F. (1990). "A methodology for knowledge acquisition in a group decision support system environment," Knowledge Acquisition, (2), 129-144.
  6. McGraw, K.L. and Harbison-Briggs, K. (1989). Knowledge Acquisition: Principles and Guidelines, Prentice Hall, Englewood Cliffs, NJ.
  7. Olson, J.R. and Rueter, H.H. (1987). "Extracting expertise from experts: Methods for knowledge acquisition," Expert Systems, (4:3), August, 152-168.
  8. Osyk, Barbara A. and Vijayaraman, Bindiganavale S. (1995). "Integrating expert systems and neural nets: Exploring the boundaries of AI," Information Systems Management, (12:2), Spring, 47-54.
  9. Prerau, D.S. (1990). Developing and Managing Expert Systems, Addison-Wesley, Reading, MA.
  10. Scott, A. Carlisle, Clayton, Jan E., and Gibson, Elizabeth L. (1991). A Practical Guide to Knowledge Acquisition, Addison-Wesley, Reading, MA.
  11. Wolstenholme, E. F. and Corben, D. A. (1994). "A hypermedia-based Delphi tool for knowledge acquisition in model building," Journal of the Operational Research Society, (45:6), June, 659-672.


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