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Agent-Based Implementation on Intelligent Instruments

  • Richard Dapoigny
  • Eric Benoit
  • Laurent Foulloy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2718)

Abstract

The use of agent to infer actions from domain specific knowledge has proved to be a successful approach. In this paper, we implement an agentbased system extracting knowledge from ontology-based databases that are embedded in intelligent instruments. As the ontology produces static information on the environment, the emerging behavior results from dependence relations between this information and the functional role of each instrument. Agents are organized in two processing agents. The first of them allows dynamic inference on data meaning. In the second agent, knowledge analysis leads to establish dependence relationships between the basic components of the instruments (i.e., variables and services) and to fire remote modes and external services. In such a way, the local model of the intelligent instrument is dynamically extended with capabilities of any other instrument.

Keywords

External Variable Remote Service External Service Information Agent Internal Service 
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. [1]
    Object Management Group, Smart Transducer Interface, OMG Document, orbos/2000-12-13, Dec. 2000Google Scholar
  2. [2]
    H. Kopetz, M. Holzmann, W. Elmenreich, A Universal Smart Transducer Interface: TTP/A, Int. Journal of Computer System Science & Engineering, 16(2), March 2001Google Scholar
  3. [3]
    M. Staroswiecki, M. Bayart, Models and languages for the interoperability of smart Instruments, Automatica, 1996, 32(6), 859–873zbMATHCrossRefMathSciNetGoogle Scholar
  4. [4]
    S. Bussmann, N.R. Jennings & M. Wooldridge, On the identification of Agents in the design of Production Control systems, AOSE, 2000, 141–162Google Scholar
  5. [5]
    E. Benoit, L. Foulloy, J. Tailland, InOMs model: a service-based approach to intelligent instrument design, 5th World Conf. On Systemics, Cybernetics and Informatics, 22–25 july 2001, Orlando (Fa)Google Scholar
  6. [6]
    E. Benoit, L. Foulloy, J. Tailland, Automatic Smart sensors Generation based on InOMs, Proc. of the 16th IMEKO World Congress, Vienna (AU), 25–28 sept. 200,9, 335–340Google Scholar
  7. [7]
    W.N. Borst, J.M. Akkermans, A. Pos & J.L. Top, The PhysSys Ontology for physical systems, 9th Int. Workshop on Qualitative Reasoning, 16–19 May 1995, 11–21Google Scholar
  8. [8]
    P. Salustri, “Function Modelling for an Integrated Framework: A progress Report”, Procs. of FLAIRS’98, D. Cook, Eds., 1998, AAAI, pp 339–343Google Scholar
  9. [9]
    T.R. Gruber, G.R. Olsen, An ontology for Engineering Mathematics, 4th Int. Conf. on Principles of Knowledge Representation and Reasoning, Bonn, 1994Google Scholar
  10. [10]
    R. Dapoigny, E. Benoit, L. Foulloy, Ontology Implementation for Knowledge Representation in Intelligent Instruments, Proc. of the 2th IEEE Int. Symp. on Signal Processing and Information Technology (dec. 2002), 17–21Google Scholar
  11. [11]
    A. Bouras, M. Staroswiecki, How can Intelligent Instruments interoperate in an Application Framework? A mechanism for taking into account operating constraints, IFAC SICICA’97, Annecy (France), June 1997, 465–472Google Scholar
  12. [12]
    D. Kinny, M. Georgeff and A. Rao, A Methodology and Modeling techniques for Systems of BDI Agents, in W. Van de Velde & J.W. Perram Eds., Springer Verlag, Procs. Of the 7th European Workshop on Modeling Autonomous Agents in a MultiAgent World, Berlin 1996, (1038), 56–71Google Scholar
  13. [13]
    S.D. Whitehead and D.H. Ballard, Learning to perceive and act by trial and error, Machine Learning, 1991, (7), 45–83Google Scholar
  14. [14]
    N. Flix & Al., Generic control/command distributed system: Application to the supervision of moving stage sets in theaters, European Journal of Control, (8), 1, 2002Google Scholar
  15. [15]
    IEC TC, Function Blocks for Industrial Process Measurement and Control Systems, Part. 1: Architecture, 1998, IEC-TC65/WG6 Committee DraftGoogle Scholar
  16. [16]
    B. Zhou, L. Wang and D.H. Norrie, Design of distributed Real-time Control Agents for Intelligent Manufacturing Systems, 2nd Int. Workshop on Intelligent Manufacturing Systems, Sept. 1999, Leuven (BE), 237–244Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Richard Dapoigny
    • 1
  • Eric Benoit
    • 1
  • Laurent Foulloy
    • 1
  1. 1.Laboratoire d’Informatique, Systèmes, Traitement de l’Information et de la ConnaissanceUniversity of SavoieAnnecy Cedex

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