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2008 | 18 | 4 | 477-486
Tytuł artykułu

Mechanical analogy of statement networks

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper demonstrates briefly the reasoning capabilities in condition monitoring offered by systems based on statement networks. The usefulness of the networks considered results among others from possibilities of their optimization related to the minimization of contradictions between rules acquired from different knowledge sources. A mechanical analogy of such networks introduces an interpretation of statements as material points that are able to move. Dependencies between statements are considered as approximate necessary and approximate sufficient conditions, which are represented by unilateral constraints imposed on the introduced material points. A model of a dynamic statement network can be obtained out of the network consisting of statements represented by material points with assigned masses, where the inertia of statements may be taken into account. The paper introduces a measure of conditional contradictions of statements, which can be used for monitoring knowledge bases in running expert systems.
Rocznik
Tom
18
Numer
4
Strony
477-486
Opis fizyczny
Daty
wydano
2008
otrzymano
2008-01-08
poprawiono
2008-05-16
Twórcy
  • Faculty of Mechanical Engineering, Silesian University of Technology, ul. Konarskiego 18a, 44-100 Gliwice, Poland
Bibliografia
  • Cholewa W. (1996). Blackboards in technical diagnostics, Proceedings of the Conference on Diagnostics of Industrial Processes, Podkowa Leśna, Poland, pp. 42-47.
  • Cholewa W. (2004). Expert systems in technical diagnostics, in J. Korbicz, J. M. Kościelny, Z. Kowalczuk and W. Cholewa (Eds.), Fault Diagnosis. Models, Artificial Intelligence, Applications, Springer, Berlin, pp. 591-631.
  • Engelmore R. and Morgan T. (Eds.) (1988). Blackboard Systems, Addison-Wesley, Reading, MA.
  • Hayes-Roth B. (1995). An architecture for adaptive intelligent systems, Artificial Intelligence 72(1-2): 329-365.
  • Isham V. (1981). An introduction to spatial point processes and Markov random fields, International Statistical Review 49(1): 21-43.
  • Jensen F. V. (2001). Bayesian Networks and Decision Graphs, Springer, New York, NY.
  • Kuncheva L. (2000). Fuzzy Classifier Design, Physica-Verlag, Heidelberg.
  • Lauritzen S. L. (1982). Lectures on Contingency Tables, 2nd Edn., University of Aalborg Press, Aalborg.
  • Moczulski W. (2004). Methods of acquisition of diagnostic knowledge, in J. Korbicz, J. Kościelny, Z. Kowalczuk and W. Cholewa (Eds.), Fault Diagnosis. Models, Artificial Intelligence, Applications, Springer, Berlin, pp. 675-718.
  • Negnevitsky M. (2002). Artificial Intelligence. A Guide to Intelligent Systems, Pearson Education Limited, Harlow.
  • Pearl J. (1988). Probabilistic Reasoning in Intelligent Systems. Networks of Plausible Inference, Morgan Kaufmann, San Mateo, CA.
  • Siler W. and Buckley J. J. (2005). Fuzzy Expert Systems and Fuzzy Reasoning, Wiley, Hoboken, NJ.
  • Wright S. (1934). The method of path coefficients, The Annals of Mathematical Statistics 5(3): 161-215.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv18i4p477bwm
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