Truth Maintenance in an Expert System with Uncertainty

• Autorzy: Jankowska B.
• Języki publikacji: EN

Abstrakty

EN

Although it is not indispensable, a truth maintenance system, or TMS, is a valuable component of an expert system. A TMS, concerned about consistency of the knowledge collected in the knowledge base, increases the reliability of conclusions obtained in a process of reasoning. The issue of TMS construction for traditional expert systems (with knowledge admitted to be certain) is well recognized from both theoretical and practical aspects. In the following paper, we present a two-level TMS project for the system with uncertainty. The presentation will be performed on the basis of an expert system used to prognosticate the effects of medical treatment of a bronchial asthma disease.

Słowa kluczowe

EN

expert systems; truth maintenance; uncertainty

• Wydawca: Wydawnictwo Uniwersytetu Jagiellońskiego
• Czasopismo: Schedae Informaticae
• Rocznik: 2005
• Tom: Vol. 14
• Strony: 61--74
• Opis fizyczny: Bibliogr. 10 poz.

Twórcy

autor

Jankowska B.

• Institute of Control and Information Engineering, Poznań Unwersity of Technology, Pl. M. Skłodowskiej-Curie 5, Poznań, 60-965 Poland,

Bibliografia

• [1] Doyle J.; A truth maintenance system, Artificial Intelligence, 12, 1979, pp. 231–272.
• [2] McDermott D.; Contexts and data dependencies: a synthesis, IEEE Transaction on Pattern Analysis and Machine Intelligence, 5 (3), 1983, pp. 237–246.
• [3]Goodwin J.; A Process Theory of Nonmonotonic Inference, in: Proc. 9th International Joint Conference on Artificial Intelligence, 1985, pp. 185–187.
• [4] de Kleer J.; An assumption-based tms, Artificial Intelligence, 28, 1986, pp. 127–162.
• [5] Mitchell T. et al.; Theo: A framework for self-improving systems, in: Architectures for Intelligence, Lawrence-Erlbaum Associates, Hillsdale, N.J., 1991, pp. 325–355.
• [6] Kahney H. et al.; Knowledge Engineering, The Open University, Scotland, 1989.
• [7] Jankowska B.; How to Aid Designing of the Knowledge Base of an Expert System with Uncertainty?, A paper submitted to The 18th International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems.
• [8] Jankowska B.; Wnioskowanie przybliżone w prognozowaniu efektów leczenia astmy oskrzelowej (Inexact reasoning in prognosticating the effects of a bronchial asthma
• [9] Jankowska B.; How to speed up reasoning in a system with uncertainty?, in: Innovations in Applied Artificial Intelligence, LNAI 3029, Springer-Verlag, 2004, pp. 817–826.
• [10] Shaffer G.; A Mathematical Theory of Evidence, Princeton University Press, 1976.