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ESPRIT ’90 pp 326-341 | Cite as

A Comparative Study of the Representation Languages Used in the Machine Learning Toolbox

  • K. Causse
  • P. Sims
  • K. Morik
  • C. Rouveirol
Conference paper

Abstract

This paper presents some early results from the Machine Learning Toolbox (MLT) project. The MLT will be a system that recommends and implements one of several machine learning algorithms or systems for an application. The learning algorithms are being contributed by various members of the consortium, and as such have been developed with their own internal knowledge representation languages. In order for the user to supply application data in a form which can be understood by more than one algorithm, and in order for any algorithm to be capable of passing its results to any other algorithm, a Common Knowledge Representation Language (CKRL) has to be developed. The first stage in this task has been to investigate the different knowledge representation languages of the tools, with the aim of emphasising their commonalities and differences. The results of this comparison are currently being used as a basis for forming the first version of a CKRL We also discuss the possible roles for the CKRL within the MLT, and select that of an interface language between the different sub-components of the MLT as being the most flexible. The CKRL aims to solve the problem of mapping entities of the epistemic level into the logic level (and vice versa) in a pragmatic way, but it will not attempt to solve the problems of the different expressive powers of each of the current algorithm’s formalisms, or to evaluate the suitability of different languages for learning.

Keywords

Learning Algorithm Domain Model Knowledge Representation Inference Engine Representation Language 
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.

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References

  1. Brachman, R. 1979: On the Epistemological Status of Semantic Networks, in: Findler (ed): Associate Networks - Representation and Use of Knowledge by Computers, Academic PressGoogle Scholar
  2. Clark, P., 1989: Deliverable 4.0 - Functional Specification of CN and AQ Google Scholar
  3. Feng, C., 1989: Deliverable 4.0 - Functional Specification of CIGOL Google Scholar
  4. Freksa, C., Furbach, U., Dirlich, G. 1984: Cognition and Representation - An Overview of Knowledge Representation Issues in Cognitive Science, in: Laubsch (ed.) Procs. of the German Workshop on Al, GWAI84, SpringerGoogle Scholar
  5. Genesereth, M.R., Nilsson, N.J. 1988 (2nd ed.): Logical Foundations of Artificial Intelligence, Morgan KaufmannGoogle Scholar
  6. INRIA, 1989: Deliverable 4.0 - Description of SICLA Google Scholar
  7. Intellisoft, 1989: Deliverable 4.0 - Description of KBG Google Scholar
  8. Intellisoft/LRI, 1989: Deliverable 4.0 - Description of APT Google Scholar
  9. Lebbe, J., Vignes, R., 1989: Deliverable 4.0 - Functional Specification of MAKEY Google Scholar
  10. Ludwig, A. 1989: Deliverable 1.1.1 - Specification of the Overall Architecture of the MLT Google Scholar
  11. Michalski, R., 1983: A Theory and Methodology of Inductive Learning, in: Michalski, Carbonell, Mitchell (eds): Machine Learning - An Artificial Intelligence Approach, Volume 1, Tioga PressGoogle Scholar
  12. Morik, K., Rouveirol, C., Sims, P. 1989: Deliverable 2.1 Comparative Study of the Representation Languages Used by the Systems of the MLT Google Scholar
  13. Niblett, T., 1989: Functional Specification if ReallD Google Scholar
  14. Quinlan, J.R., 1983: Learning Efficient Classification Procedures and their Application to Chess End Games, in: Michalski, Carbonell, Mitchell (eds): Machine Learning - An Artificial Intelligence Approach, Volume 1, Tioga PressGoogle Scholar
  15. Parsons, T., 1989: Deliverable 4.0 - The DMP, Description and Status Google Scholar
  16. Ralambondrainy, H., 1989 How to deal with categorical data using clustering methods in: R. Coppi and S.Bolasco (eds): Multiway Data Analysis, North HollandGoogle Scholar
  17. Sims, P., 1989: Deliverable 4.0 - LASH Algorithm Description Google Scholar
  18. Sleeman, D., Oehlmann, R., Davidge, R. 1989: Deliverable 5.0 - Specification of Consultant-0 Google Scholar
  19. Wrobel, S., 1989: Deliverable 4.0 - Description of MOBAL Google Scholar

Copyright information

© ECSC, EEC, EAEC, Brussels and Luxembourg 1990

Authors and Affiliations

  • K. Causse
    • 1
  • P. Sims
    • 2
  • K. Morik
    • 3
  • C. Rouveirol
    • 4
  1. 1.IntellisoftOrsayFrance
  2. 2.Sowerby Research CentreBritish Aerospace PlcFilton, BristolEngland
  3. 3.Gesellscaft fur Mathematik und DatenverabeitungSankt Augustin 1Germany
  4. 4.Laboratoire de Recherche en InformatiqueUniversité de Paris-SudOrsayFrance

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