Knowledge Architectures for Real Time Decision Support

  • José Cuena
Conference paper


This chapter deals with the presentation, illustrated by examples, of an architecture aiming to the representation of knowledge for real time decision support for physical systems management. However the proposed approach may be used to deal with other decision support systems where a level of behavior modeling be required.

First, the concept of representation based on knowledge level specification of an agent with subsequent modeling using generic tasks is presented. Second, the general pattern for reasoning and knowledge structuring of an agent for decision support on physical systems is presented together with some considerations on the alternative approaches for physical behavior modeling. Third, the example of real time flood management agent is discussed. A knowledge level structure is proposed initially and two symbolic level representations are proposed addressing the specification: the CYRAH and SIRAH architectures. Both approaches are described from the criteria for representation and inference to the operational aspects.

Fourth, to show the possibilities of the approach in a different field the real time traffic management problem is analyzed proposing an architecture adapted to a pattern for reasoning for traffic control strategy real time adaptation to present and emerging problems. This approach is now in course of implementation in several specific projects.

Finally, some comments are proposed on integration of knowledge based approach and conventional software approach suggested by these experiences.


Traffic Control Generic Task Inference Engine Reception Area Flood Management 
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.
    Newell A.: “The Knowledge Level” In Artificial Intelligence Vol 18 pp 87–127.Google Scholar
  2. 2.
    Chandrasekaran B.: “Towards a Taxonomy of Problem Solving Types” A.I. Magazine 4(1) 9–17, 1983.Google Scholar
  3. 3.
    Chandrasekaran, B.: “Generic Tasks in Knowledge Based Reasoning: High Level Building Blocks for Expert Systems Design” IEEE Expert, 1986.Google Scholar
  4. 4.
    Cuena J.: “Knowledge-Based Systems for Aid in Decision-Making: Methodology and Examples” included in “Perspectives in Artificial Intelligence” vol. 1, 73–92. (Campbell J.A., Cuena J. eds.) Ellis Horwood, 1989.Google Scholar
  5. 5.
    Chow V.T, Maidment D.R., Mays L.W.: “Applied Hydrology” Chap. 10. Mc Graw Hill, 1988.Google Scholar
  6. 6.
    Cuena J.: “Building Expert Systems Based on Simulation Models: An Essay on Methodology” included in “Expert System Applications ( Bolc, Coombs eds). Springer Verlag, 1988.Google Scholar
  7. 7.
    Winston P.H.: “The commercial Debut of Artificial Intelligence” in “Applications of Expert Systems” Quinlan J.R. (ed) Addison Wesley, 1987.Google Scholar
  8. 8.
    Hayes P.J.: “The Naive Physics Manifesto” in Michie D. (ed) “Expert Systems in the Microelectronic Age”. Edinburgh University Press, 1979.Google Scholar
  9. 9.
    De Kleer, J., Brown, S.: “A Qualitative Physics Based on Confluences” Artificial Intelligence 24. Elsevier Science Publishers B.V. (North Holland) 1984, 7–83.Google Scholar
  10. 10.
    Forbus K.D.: “Qualitative Process Theory” Artificial Intelligence 24, 85–108, 1984.CrossRefGoogle Scholar
  11. 11.
    Kuipers BX: “Qualitative Simulation” Artificial Intelligence 29, 289–338.Google Scholar
  12. 12.
    Weld M., De Kleer J.: “Readings in Qualitative Physics” Morgan Kaufmann, 1990.Google Scholar
  13. 13.
    Weigend A.S., Huberman B.A., Rumelhart D.R.: “Predicting the Future: A Connectionist Approach” Technical Report SSL-90-20, Xerox Palo Alto Research Center (1990).Google Scholar
  14. 14.
    Alonso M., Cuena J., Molina M.: “SIRAH: An Architecture for a Professional Intelligence”. Proc.9th European Conference on Artificial Intelligence (ECAI’90). Pitman, 1990.Google Scholar
  15. 15.
    Cuena J., Molina M., Garrote L.: “An Architecture for Cooperation of Knowledge Bases and Quantitative Models: The CYRAH Environment”. XI International Workshop on Expert Systems. Special conference on Second Generation Expert Systems. Avignon’91. EC2, 1991.Google Scholar
  16. 16.
    De Kleer, J., Brown, S.: “A Qualitative Physics Based on Confluences” Artificial Intelligence 24. Elsevier Science Publishers B.V. (North Holland) 1984, 7–83.Google Scholar
  17. 17.
    Cuena J.: “Intelligent Systems for Traffic Flow Management: A Qualitative Modeling Approach” International Journal of Intelligent Systems, Vol 7, 133–153, 1992.CrossRefGoogle Scholar
  18. 18.
    Cuena J., Martin G., Molina M.: “An Architecture for Knowledge Based Traffic Management for the EXPO-92 Sevilla Urban Ring”. Proc. Second International OECD Workshop on Knowledge-Based Expert Systems in Transportation. Montreal, June 1992.Google Scholar
  19. 19.
    Cuena J., Ambrosino G., Boero M.: “A General Knowledge-Based Architecture for Traffic Control: The KITS Approach”. Proc. International Conference on Artificial Intelligence Applications in Transportation Engineering. San Buenaventura, California. June 1992.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • José Cuena
    • 1
  1. 1.Departamento de Inteligencia ArtificialUniversidad Politécnica de MadridBoadilla del Monte MadridSpain

Personalised recommendations