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Tytuł artykułu

Analiza zastosowań systemów komputerowych wykorzystujących sieci Bayesa

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Identyfikatory
Warianty tytułu
EN
Hybrid systems - combination of expert systems and neural network
Języki publikacji
PL
Abstrakty
PL
Celem artykułu jest analiza możliwości praktycznego wykorzystania technologii opartych na sieciach Bayesa w systemach komputerowych. Przedstawiono klasy zastosowań sieci Bayesa, aplikacje będące w fazie projektu, a także przykłady komercyjnych systemów z różnych dziedzin. W ramach przykładu omówiono technologie Answer Wizard i Office Assistant, zastosowane w systemie pomocy dla użytkowników aplikacji biurowych. Przedstawiono wady i zalety sieci Bayesa w kontekście zastosowań praktycznych.
EN
The main goal of the article is to analyze possibilities of the practical use of technologies based on Bayesian Networks in the computer systems. The article presents applications of classes of Bayesian Networks, systems being actually developed and examples of commercial systems from different fields. As an example, two technologies are discussed: Answer Wizard and Office Assistant. Furthermore, advantages and disadvantages of Bayesian Networks in the context of practical use are presented.
Rocznik
Strony
53--69
Opis fizyczny
Bibliogr. 35 poz., schem.
Twórcy
autor
  • Katedra Automatyki, Elektroniki i Informatyki, Politechnika Opolska
Bibliografia
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  • [15] HEJLESEN O.K. et al.: Implementation of learning procedure for multiple observations in a diabetes advisory system based on casual probabilistic networks, Proceedings of the Fourth Conference on Artificial Intelligence in Medicine, S. Andreassen et al. (eds), Amsterdam: IOS, 1993.
  • [16] HORVITZ E.: Agents With Beliefs: Reflections on Bayesian Methods for User Modeling, Proceedings of the Sixth International Conference on User Modeling, Sardinia, Italy, June, 1997.
  • [17] HORVITZ E., BARRY B.: Display of information for time-critical decision making, Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, Besnard & Hanks (eds), San Francisco, CA: Morgan Kaufmann, 1995.
  • [18] HORVITZ E.: Lumiere Bayesian Reasoning, User Modeling and Automated Assistance, http://research.microsoft.com/~horvitz/lum.htm
  • [19] HORVITZ E.: The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users, Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, 1998.
  • [20] JENSEN F. V.: An Introduction to Bayesian networks, UCL Press Ltd, London, 1996.
  • [21] LAURITZEN S.L. et al.: Diagnostic systems created by model selection methods: a case study, Selecting models from data: Al and statistics IV (Lecture Notes in Statistics), Cheeseman & Oldford (eds), New York, Springer, 1994.
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  • [27] RASMUSSEN L.K.: Bayesian network for blood typing and parentage verification of cattle, Dina research report 38, Aalborg University, Department of Mathematics and Computer Science, 1995.
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  • [29] RIMEY R., BROWN C.: Control of selective perception using Bayes nets and decision theory, International Journal of Computer Vision 12, Special issue in Active Vision, 1994.
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  • [31] SUCHACKA G., ZGRZYWA A.: Charakterystyka sieci Bayesa jako metody wnioskowania w warunkach niepełnych i niepewnych danych, ZN Politechniki Opolskiej, nr 282/2001, seria Informatyka, z. 1, s. 29-52.
  • [32] XIANG Y. et al.: Multiply sectioned Bayesian networks for neuromuscular diagnosis, Artificial Intelligence in Medicine 5 (4), 1993.
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  • [35] Deployed Bayesian - Nets Systems in Routine Use, http://www.csualberta.ca/~greiner/bn.html
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPOG-0006-0004
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