Multiple Models for Emergency Planning

  • Olivier Paillet
Conference paper


The Second Generation Expert Systems approach begins to be widely used in areas such as diagnosis. In this paper, we will demonstrate its potential use to solve real-world problems in planning. This approach is illustrated by an expert system that builds restoration plans after a failure on a power transmission network. Organized around a blackboard, it integrates planning knowledge sources containing restoration expertise, a qualitative model used to predict the results of the plan, and a quantitative model used to verify the correctness of the plans towards numerical constraints. We will describe the benefits of this architecture for emergency planning systems, and the possibilities offered by the coupling of models and heuristics, for instance to reason with incomplete information.


Knowledge Source Emergency Planning Qualitative Model Restoration Plan Partial Plan 
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.
    C. Benoit, M. Bidoit, L. Henninger, R. Velly: LORE: an Object-based Programming Environment, TOOLS 89, Paris, november 1989Google Scholar
  2. 2.
    J.C. Bonnet, M. Uszynski: RAMSES: a Second Generation Expert System for Train Maintenance, Avignon 89, specialized conference on Second Generation Expert SystemsGoogle Scholar
  3. 3.
    B. Chandrasekaran: Towards a functional architecture for intelligence based on generic information processing tasks, IJCAI87, Milan 1987Google Scholar
  4. 4.
    D. Chapman: Planning for Conjunctive Goals, AI Journal, Vol. 32, 1987Google Scholar
  5. 5.
    J.M. David, J.P. Krivine: Designing Knowledge-based Systems within Functional Architecture: the DIVA Experiment, CAIA89, Miami 1989Google Scholar
  6. 6.
    R. Davis: Diagnostic Reasoning based on Structure and Behaviour, Artificial Intelligence 24, 1984Google Scholar
  7. 7.
    T. Dean, M. Boddy: An Analysis of Time-Dependant Reasoning, AAAI 88, Saint Paul, Minnesota, 1988Google Scholar
  8. 8.
    R. Engelmore, T. Morgan: Blackboard Systems — Addison Wesley 1988Google Scholar
  9. 9.
    H. Fargier, O. Paillet: A Framework for Blackboard Applications, AAAI Workshop on Blackboard Systems, AAAI 91, Anaheim, 1991Google Scholar
  10. 10.
    R. Fikes, N. Nillson: STRIPS, a New Approach to the Application of Theorem Proving to Problem-Solving, Artificial Intelligence n.2, 1974Google Scholar
  11. 11.
    S. Feray-Beaumont, S. Gentil, L. Leyval: Declarative modelling for process supervision, Revue d’Intelligence Artificielle, Vol.3, n.4, 1989Google Scholar
  12. 12.
    P. Fink: Control and integration of Diverse Knowledge in a Diagnostic Expert System, IJCAI 85Google Scholar
  13. 13.
    B. Hayes-Roth, F. Hayes-Roth, S. Rosenschein, S. Cammarata: Modeling Planning as an Incremental, Opportunistic Process — IJCAI 79Google Scholar
  14. 14.
    B. Hayes-Roth: A Blackboard Architecture for Control-Artificial Intelligence, Vol. 26, No3, 1985Google Scholar
  15. 15.
    M.R. Herbert, G.H. Williams: An Initial Evaluation of the Detection and Diagnosis of Power Plant Faults using a Deep Knowledge Representation of Physical Behaviour, Expert Systems, Vol.4, n.2, May 1987Google Scholar
  16. 16.
    F. Jakob, P. Suslenschi: Situation Assessment for Process Control, Robotics and Computer-Integrated Manufacturing, Vol.3, n.2, 1987Google Scholar
  17. 17.
    A. Paterson, P. Sachs, M. Turner: ESCORT: the Application of Causal Knowledge to Real-time Process Control, Expert Systems 85, Cambridge University Press, 1985Google Scholar
  18. 18.
    W. Swartout ed.: DARPA Santa Cruz Workshop on Planning, AI Magazine, Summer 1988Google Scholar
  19. 19.
    Hwee Tou Ng: Model-based, Multiple-Fault Diagnosis of Dynamic, Continuous Physical Devices, IEEE Expert, December 1991Google Scholar
  20. 20.
    D. Wilkins: Domain Independant Planning: Representation and Plan Generation, Artificial Intelligence n.22, 1984Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Olivier Paillet
    • 1
  1. 1.Alcatel Alsthom RechercheMarcoussisFrance

Personalised recommendations