Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-05T09:06:59.186Z Has data issue: false hasContentIssue false
  • English
  • Français

Adaptive uncertainty management for a class of diagnostic expert systems

Published online by Cambridge University Press:  27 February 2009

Nikola Bogunović  and
Tomislav Mesić
Nikola Bogunović
Affiliation:
Rudjer Bošković Institute, Zagreb, Croatia
Tomislav Mesić
Affiliation:
Rudjer Bošković Institute, Zagreb, Croatia
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper gives a theoretical foundation and implementation details of data and control structures in a diagnostic expert system with adaptive uncertainty management. The considered problem covers a class of expert systems and the application domain in which the operating personnel actively monitor and control the outcome of an industrial manufacturing process. Based on subjective ranking of the latent fault in the final output product, the operator queries the expert system for a recommended corrective action. Subsequent successful or unsuccessful corrective actions consistently modify confidence measures of all corrective recommendations from the same fault set, according to the embedded analytical model. The described approach is based on the relational data representation and procedural control structures, as opposed to a more traditional declarative, production-based system.

Keywords

Expert SystemsProcess DiagnosisUncertainty Management
Type
Articles
Information
AI EDAM , Volume 10 , Issue 5 , November 1996 , pp. 421 - 429
Copyright
Copyright © Cambridge University Press 1996

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Apt, K.R., Blair, H.A., & Walker, A. (1988). Towards a theory of declarative knowledge. In Foundations of Deductive Databases and Logic Programming, (Minker, J., Ed.) pp. 89–148. Morgan Kaufmann, Los Altos, California.CrossRefGoogle Scholar
Buchanan, B.G., & Shortliffe, E.H. (Eds.).(1984). Rule-based expert systems: The MYCIN experiments of the Stanford Heuristic Programming Project. Addison-Wesley, Reading, MA.Google Scholar
Cendrowska, J., & Bramer, M.A. (1984). A rational reconstruction of the MYCIN consultation system. Int. J. Man-Machine Studies 20, 229–317.CrossRefGoogle Scholar
Charniak, E., & McDermott, D. (1987). Introduction to artificial intelligence (Chapt. 8). Addison-Wesley, Reading, MA.Google Scholar
Cooper, G., (1990). Computational complexity of probabilistic inference using Bayesian belief networks. Artif. Intell. 42, 393–405.CrossRefGoogle Scholar
Dagum, P., & Luby, M. (1993). Approximating probabilistic inference in Bayesian belief networks is NP-hard. Artif. Intell. 60, 141–153.CrossRefGoogle Scholar
Dubois, D., & Prade, H. (1980). Fuzzy sets and systems: Theory and application. Academic Press, New York.Google Scholar
Feigenbaum, E. (1994). Knowledge-based systems research and application in Japan. Al Magazine 15(2), 28–43.Google Scholar
Heckerman, D., & Wellman, M.P. (1995). Bayesian networks. Communications of the ACM 38(3), 27–30.CrossRefGoogle Scholar
Lloyd, J.W. (1993). Foundations of logic programming. Springer-Verlag, Berlin.Google Scholar
Mizogutchi, R., & Motoda, N. (1995). Expert system research in Japan. IEEE Expert 10(4), 14–23.CrossRefGoogle Scholar
Ng, K., & Abramson, B. (1990). Uncertainty management in expert systems. IEEE Expert 5(2), 29–47.CrossRefGoogle Scholar
Nilsson, N.J. (1986). Probabilistic logic. Artif. Intell. 28, 71–87.CrossRefGoogle Scholar
Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann, San Mateo, California.Google Scholar
Shafer, G. (1976). A mathematical theory of evidence. Princeton University Press, Princeton, New Jersey.CrossRefGoogle Scholar
Shenoy, P.P. (1993). Valuation networks, decision trees, and influence diagrams: A comparison. In Uncertainty in Intelligent Systems (Bouchon-Meunier, B., et al. , Eds.), pp. 3–14, North-Holland, Amsterdam, The Netherlands.Google Scholar