This paper argues that, contrary to the expectations of researchers, academics and practitioners, current expert systems (ES) for business information and decision making applications are actually quite primitive and fall disturbingly short of claims of intelligence. The focus of the article is a discussion of the problems with expert systems and a realistic assessment of the narrow spectrum of ES successes. The problems seriously detract from the prospects of expert systems as practical enhancements to most present-day business information systems.
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References
1.
AbdolmohammadiM.J. (1987). Decision support and expert systems in auditing: a review and research directions. Accounting and Business Research, Spring, 173–184.
BarrA., and FeigenbaumE.A. (eds) (1981). The Handbook of Artificial Intelligence, Volume I (Addison-Wesley, Reading, MA).
4.
BeckmanT.J. (1991). Selecting expert-system applications. AI Expert, 6, 42–48.
5.
BinbasiogluM., AndjarkeM. (1986). Domain specific DSS tools for knowledge-based model building. Decision Support Systems, 2, 213–223.
6.
BouwmanM.J. (1983). Human diagnostic reasoning by computer: an illustration from financial analysis. Management Science, June, 653–672.
7.
BramerM. (1982). A survey and critical review of expert systems research, in Introductory Readings in Expert Systems, MichieD. (ed) (Gordon and Breach, New York).
8.
BuchananB.G. (1986). Expert systems: working systems and the research literature. Expert Systems, 3, 32–51.
9.
BuchananB.G., and ShortliffeE.B. (eds) (1985). Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project (Addison-Wesley, Reading, MA).
10.
ConnellN.A.D. (1987). Expert Systems in Accountancy: A Review of Some Recent Applications. Accounting and Business Research, Summer, 221–233.
11.
CorcoranE. (1991). Sorting out chaos on Wall Street. Scientific American, June, 121.
12.
FriedL. (1987). The dangers of dabbling in expert systems. Computerworld, 29 June, 65–72.
13.
GoulM. (1987). On building expert systems for strategic planners. Information and Management, November, 131–141.
14.
Hayes-RothF., WatermanD., and LenatD. (1983). Building Expert Systems (Addison-Wesley, Reading, MA).
15.
JohnsonB. (1986). A survey of current AI applications in business. Research paper for Arthur Anderson and Company.
16.
Leonard BartonD., and SvioklaJ.J. (1988). Putting expert systems to work. Harvard Business Review, March/April, 91–98.
17.
LiangT. (1988). Expert systems as decision aids: issues and strategies. Bureau of Economic and Business Research Faculty Working Paper No. 1378, University of Illinois, Urbana-Champaign.
18.
LinE. (1986). Expert systems for business applications: potential and limitations. Journal of Systems Management, July, 18–21.
19.
LuconiF.L., MaloneT.W., and Scott MortonM.S. (1986). Expert systems: the next challenge for managers. Sloan Management Review, Summer, 3–14.
20.
MartinsG. (1984). The overselling of expert systems. Datamation, November, 76–80.
21.
MichalskiR.S. (1983). A theory and methodology of inductive learning, in Machine Learning, Michalski, R:, CarbonellJ., and MitchellT. (eds), (Tioga Publishing Co., Palo Alto, CA).
22.
OlsonJ.R., and RueterH.H. (1987). Extracting expertise from experts: methods for knowledge acquisition. Expert Systems, August, 152–168.
23.
RibarG., ArcoleoF., and HolloD. (1991). Loan probe: testing a big expert system. AI Expert, 6, 43–49.
24.
SacerdotiE.D. (1991). Managing Expert-System Development. AI Expert, 6, 26–33.
