Harmonisation of Soft Logical Inference Rules in Distributed Decision Systems

  • Juliusz L. Kulikowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4252)


It is considered a problem of harmonisation of diagnostic rules used in a distributed set of diagnostic centres, the rules being based on a k-most-similar-cases approach. Harmonisation is reached due to an exchange of diagnostic cases among the reference sets stored in the centres. The method is based on general concepts of similarity, semi-similarity and structural compatibilitymeasures used to evaluation of adequacy of records in remote data files to the requirements connected with supporting decision making in a given, local diagnostic centre. The procedure of local reference set extension by diagnostic cases selection and acquisition is described.


Diagnostic Centre Structure Record Structural Compatibility Diagnostic Case Final Decision Making 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kurzynski, M., Sas, J., Blinowska, A.: Rule-Based Medical Decision Making via Unification Procedure of Information. In: Proc. 13th Int. Congress of Medical Informatics Europe, Copenhagen, vol. A, pp. 537–541 (1996)Google Scholar
  2. 2.
    Hart, P.E.: The Condensed Nearest Neighbour Rule. IEEE Trans. Information Theory 14(3), 515–516 (1968)CrossRefGoogle Scholar
  3. 3.
    Dastani, M., Gomez-Sanz, J.J.: Programming Multi-agent Systems. The Knowledge Engineering Rev. 20(2), 151–164 (2005)CrossRefGoogle Scholar
  4. 4.
    Di Marzo Serugendo, G., Gleizes, M.-P., Karageorgos, A.: Self-organization in Multi-agent Systems. The Knowledge Engineering Rev. 20(2), 165–189 (2005)CrossRefGoogle Scholar
  5. 5.
    Kulikowski, J.L.: Pattern Recognition Based on Ambiguous Indications of Experts. In: Komputerowe Systemy Rozpoznawania KOSYR 2001 (pod red. M. Kurzyńskiego). Wyd. Politechniki Wrocławskiej, Wrocław 2001, pp. 15–22 (2001)Google Scholar
  6. 6.
    Borgo, S., Guarino, N., Masolo, C., Vetere, G.: Using a Large Linguistic Ontology for Internet Based Retrieval of Object-Oriented Components. In: Proc. of the Conf. on Software Engineering and Knowledge Engineering, pp. 528–534 (1997)Google Scholar
  7. 7.
    Mayfield, J.: Ontologies and Text Retrieval. The Knowledge Eng. Rev. 17(1), 71–75 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Juliusz L. Kulikowski
    • 1
  1. 1.Institute of Biocybernetics and Biomedical Engineering PASWarsawPoland

Personalised recommendations