Journal of Intelligent Manufacturing

, Volume 16, Issue 4–5, pp 515–525 | Cite as

A distributed semantic network model for a collaborative intelligent system

  • Virgilio López-Morales
  • Omar López-Ortega


A methodology based on topology theory to model a semantic network for a collaborative system is given. This framework is used to support the creation of a semantic network and to define the associated intelligent cooperative system. Our methodology is illustrated via a set of agents whose knowledge-base is a semantic network. By a series of functions applied on a base of entities, issued from the application domain, a family of sets are synthesized with their subspaces correlated. The resultant subspaces and their relations form a network of elementary and complex concepts that can be naturally represented with the IDEF1x language. A prototype Multi-Agent System (MAS), set up with the Zeus platform,1 was developed for the Process Plan domain, which was used as a case study. Full correspondence among the subspaces, the semantic network IDEF1x information model and the MAS implementation is obtained by employing this framework.


Process planning multi-agent systems semantic network topological structure 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Anality, A., Spyratos, N., Constantopoulos, P. 1998On the semantics of a semantic networkFundamenta Informaticae.33134Google Scholar
  2. Balakrishnan, A.V. 1982Applied Functional AnalysisSpringer – VerlagBerlin.Google Scholar
  3. Codd E.F. (1969) Derivability, redundancy and consistency of relations stored in large data banks. IBM research report RJ599. August.Google Scholar
  4. Date, C.J. 2000An Introduction to Database Systems, 7th editionAddison – WesleyUSA.Google Scholar
  5. Dong J., Parsaei, H., and Kumar, A. (1995). Intelligent Feature Extraction for Concurrent Design and Manufacturing, H. Parsaei (ed), Prentice Hall. London.Google Scholar
  6. Dugundji, J. 1966TopologyAllyn and BaconBoston, MA.Google Scholar
  7. FIPA (2003) Foundation for intelligent physical agents. Scholar
  8. Gu, P., Balasubramnian, S., Norrie, D.H. 1997Bidding based process planning and scheduling in a multi agent systemComputers Ind. Engng.32477496CrossRefGoogle Scholar
  9. Jennings, N.R. 1999Agent oriented software engineeringLecture Notes in Artificial Intelligence.1661410Google Scholar
  10. Kusiak, A., Letsche, T., Zakarian, A. 1997Data modelling with IDEF1xInternational Journal of Computer Integrated Manufacturing.10470486CrossRefGoogle Scholar
  11. LeDoux J. (1986) Sensory systems and emotions. Integrative Psychiatry. 4Google Scholar
  12. Li, P. 2001Modelling of integrated, distributed and cooperative system using an agent based approachProceedings of the Institute of Mechanical Engineers – Part B – Manufacturing Engineering.2(October)14371452Google Scholar
  13. Lihui W., Shen W. (2001) Agent based decision making for distributed process planning. Third ICSC – NAISO World Manufacturing Congress.Google Scholar
  14. López-Morales, V., Glumineau, A., Plestan, F. 2001An algorithm for the structural analysis of state space: synthesis of nonlinear observersInternational Journal of Robust and Nonlinear Control.1111451160CrossRefGoogle Scholar
  15. López-Morales, V., López-Ortega, O. 2003A multi-agents system to build process plans based on sub-spaces.Computer, communications and Control Technologies. Proceedings of the IIIS - CCCT International Conference.IV3438Google Scholar
  16. López-Ortega, O. 2001A Java application based on an EXPRESS model for sharing flexible manufacturing resources data.Proceedings of the 2001 IEEE International Conference on Emerging Technologies and Factory Automation.2311CrossRefGoogle Scholar
  17. López-Ortega, O. 2002Design and implementation of an open manufacturing information system to enhance data sharing and exchanging among applications.Proceedings of the 2002 IEEE International Symposium on Industrial Electronics1245253CrossRefGoogle Scholar
  18. López-Ortega O., and López-Morales, V. (2003) Integrated modelling of facts and tasks in a multi-agents system for building process plans. Applications of informatics and cybernetics in science and engineering. Proceedings of the SCI 2003, Orlando, Florida, USA. VII, 165–169.Google Scholar
  19. Moulin B., Chaib-Draa B. (1996) In Foundations of Distribured Artificial Intelligence – An overview of distributed artificial intelligence, G. O’Hare and N. Jennings (eds), John Wiley and Sons, USA.Google Scholar
  20. Nwana H. and Ndumu, D. (1999) A perspective on software agents research. The knowledge engineering review, January.Google Scholar
  21. Preuss, G. 2002Foundations of TopologyAn Approach to Convenient Topology. Kluwer Academic PublisherThe Netherlands.Google Scholar
  22. Shen, W., Maturana, F., Norrie, D. 2000MetaMorph II: An agent based architecture for distributed intelligent design and manufacturingJournal of Intelligent Manufacturing.11237251CrossRefGoogle Scholar
  23. Tecuci, G. 1998Building Intelligent Agents: An Apprenticeship Multistrategy Learning theory, Methodology, Tool and Case StudiesAcademic PressLondon.Google Scholar
  24. Usher J. (1995) In Object oriented approach to feature based process planning H.Parsaei (ed), Prentice Hall, London.Google Scholar
  25. Zhao, F.L. 2000Cooperative agent modelling approach for process planningComputers in Industry.418397CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  1. 1.Research Center on Information Technologies and Systems (CITIS)Universidad Autónoma del Estado de HidalgoPachucaMéxico

Personalised recommendations