Journal of Intelligent and Robotic Systems

, Volume 41, Issue 2–3, pp 113–140 | Cite as

Modelling structures in generic space, a condition for adaptiveness of monitoring cognitive agent

  • Marc Le Goc
  • Michel Gaeta


The adaptiveness of agents is one of the basic conditions for the autonomy. This paper describes an approach of adaptiveness forMonitoring Cognitive Agents based on the notion of “generic spaces”. This notion allows the definition of virtual “generic processes” so that any particular actual process is then a simple configuration of the “generic process”, that is to say a set of values of parameters. Consequently, “generic domain ontology” containing the generic knowledge for solving problems concerning the “generic process” can be developed. This lead to the design of “Generic Monitoring Cognitive Agent”, a class of agent in which the whole knowledge corpus is generic. In other words, modeling a process within a generic space becomes configuring a “generic process” and adaptiveness becomes genericity, that is to say independence regarding technology. In this paper, we present an application of this approach on Sachem, a Generic Monitoring Cognitive Agent designed in order to help the operators in operating a blast furnace. Specifically, the “NeuroGaz” module of Sachem will be used to present the notion of a “generic blast furnace”. The adaptiveness of Sachem can then be noted through the low cost of the deployment of a Sachem instance on different blast furnaces and the ability of “NeuroGaz” in solving problem and learning from various top gas instrumentation.

Key words

autonomous agent cognitive agent expert systems diagnosis monitoring 


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  1. Brazier, F. M. T., Jonker, C. M., Treur, J., Wijngaards, N. J. E. 1999Deliberate evolution in multi-agent systemsEtzioni, O.Mueller, J. P.Bradshaw, J. eds. Proc. of the Third Annual Conf. on Autonomous Agents, Agents’99ACM PressNew York356357Google Scholar
  2. Cauvin, S.,  et al. 1998Monitoring and alarm interpretation in industrial environmentsAICOMS11139173Google Scholar
  3. Frydman, C., Goc, M., Torres, L., Giambiasi, N. 2001Knowledge-based diagnosis in SACHEM using DEVS modelsSpecial Issues on Recent Advances in DEVS Methodology of Trans. Soc. Modeling Simulation Internat. (SCS)18147158Google Scholar
  4. Gruber, T. R.: 1993a, A translation approach to portable ontology specifications, Knowledge Acquisition 5(2).Google Scholar
  5. Gruber, T. R.: 1993b, Toward principles for the design of ontologies used for knowledge sharing, in: N. Guarino and R. Poli (eds), Formal Ontology in Conceptual Analysis and Knowledge Representation, Kluwer Academic, in preparation; original paper presented at the Internat. Workshop on Formal Ontology, March 1993.Google Scholar
  6. Goc, M., Frydman, C., Torres, L. 2002Verification and validation of the sachem conceptual modelInternat. J. Human Computer Studies56199223Google Scholar
  7. Le Goc, M., Frydman, C., Torres, L., and Giambiasi, N.: 2000, Knowledge-based discrete event models for computer aided diagnosis, in: The 4th World Multiconference on Systemics, Cybernetics and Informatics SCI 2000, Orlando, USA, July.Google Scholar
  8. Le Goc, M. and Thirion, C.: 1999, Using both numerical and symbolic models to create economic value: The SACHEM system example, in: Proc. of the 27th McMaster Symposium on Iron and Steelmaking, Hamilton, ON, Canada, May.Google Scholar
  9. Le Goc, M., Touzet, C., and Thirion, C.: 1998, The SACHEM experience on ANN application, Invited paper at Neurap’98, Fourth Internat. Conf. on Neural Networks and Their Applications, Marseille, France, May, pp. 315–321.Google Scholar
  10. Le Moigne, J.-L.: 1984, La Théorie du Système Général - Théorie de la Modélisation, Presse Universitaire de France.Google Scholar
  11. Newell, A.: 1982, The knowledge level, Artificial Intelligence 18.Google Scholar
  12. Page, E.: 1994, Simulation modeling methodology: Principles and etiology of decision support, PhD Thesis, Virginia Polytechnic Institute and State University.Google Scholar
  13. Schreiber, G., Akkermans, H., Anjewierden, A., Hoog, R., Shaldbolt, N., Velde, W., Wielinga, B. 2000Knowledge Engineering and Management - The Common KADS MethodologyMIT PressCambridge, MA, USAGoogle Scholar
  14. Valente, A., Breuker, J., and Van de Velde, W.: 1994, The CommonKADS expertise modeling library, in: J. Breuker and W. Van de Velde (eds), CommonKADS Library for Expertise Modelling. Reusable Problem Solving Components, IOS Press and Ohmsha.Google Scholar
  15. Zeigler, B. 1984DEVS Multifaceted Modeling and Discrete Event SimulationAcademic PressLondon, UKGoogle Scholar
  16. Zouaoui, F.: 1998, Aide à l’interprétation du fonctionnement des systèmes physiques en utilisant une approche multi-modèle. Application au circuit primaire d’une centrale à eau pressurisée, Thèse, Université de Paris XI-Orsay.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Marc Le Goc
    • 1
    • 2
  • Michel Gaeta
    • 3
  1. 1.Polytech’ MarseilleLSIS, UMR CNRS 6168Marseille Cedex 20France
  2. 2.TIXIS Systems, ARCELOR Group, SACHEM ResearchFos sur Mer CedexFrance
  3. 3.VIBRIALa Seyne sur MerFrance

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