Metacontrol of a Traffic Simulator Using Situation Semantics

  • Harold José Paredes-Frigolett


In the present article, we present how concepts from Situation Semantics ([2], [6]) can be used for the metacontrol tasks of a traffic simulator. Situation Semantics, as presented in [2], is one of the most ambitious theories for knowledge representation, especially for representing the meaning of natural language. The theory can also be used in current AI systems to represent the knowledge of a variety of domains. We show how concepts from Situation Semantics can be used for the metacontrol tasks of a traffic simulator. We address not only formal aspects of a situational theory for our traffic domain, but also present a representation language by means of which different traffic situations can be represented.


Representation Language Location Constraint Traffic Situation Traffic Simulator Argument Position 
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]
    Allen J. F. and Koomen J. A. Planning using a temporal world model, Proceedings of the 8th International Joint Conference on Artificial Intelligence, IJCAI, 1983.Google Scholar
  2. [2]
    Barwise J. and Perry J. Situations and Attitudes, The MIT Press, 1983.Google Scholar
  3. [3]
    Backofen R., Trost H. and Uszkoreit H. Linking typed feature formalisms and terminological knowledge representation languages in natural languages front-ends, 375-383, in Brauer and Hernández (editors), Verteilte Künstliche Intelligenz und Kooperatives Arbeiten, 4. Internationaler Kongreß Wissensbasierte Systeme, München, 1991.Google Scholar
  4. [4]
    Branchman R. J. and Schmolze J. G. An overview of the KL-ONE knowledge representation system, Cognitive Science, 9(2): 171–216, April–June, 1985.CrossRefGoogle Scholar
  5. [5]
    Davidson D. The logical form of action sentences, in Nicholas Rescher (editor), The Logic of Decision and Action, 81–95. University of Pittsburgh Press, Pittsburgh, 1967.Google Scholar
  6. [6]
    Fenstad J.E., Halvorsen P.K., Langholm T., and van Benthem J. Situations, Language and Logic, Reidel, 1987.Google Scholar
  7. [7]
    Nebel B. and Smolka G. Representation and reasoning with attributive descriptions, in K. H. Blaesius, U. Hedstueck and C. R. Rollinger (editors), Sorts and Types in Artificial Intelligence, Springer, Berlin, 1989.Google Scholar
  8. [8]
    Paredes-Frigolett, H. J. and Bickenbach H. J. Using artificial intelligence methods for the development of a traffic simulator, Proceedings of the Fourth International Training and Equipment Conference and Exhibition, ITEC-93, 79-85, London, 1993.Google Scholar
  9. [9]
    Pollard C. J. and Sag I. A. Unifying partial descriptions of sets, in Hansen P. (editor), Information, Language and Cognition: Vancouver Studies in Cognitive Science, Volume 1, University of British Columbia Press, Vancouver, 1989.Google Scholar

Copyright information

© Springer Science+Business Media New York 1994

Authors and Affiliations

  • Harold José Paredes-Frigolett
    • 1
  1. 1.AITEC GmbH & Co. Angewandte Informationstechnologie KGDortmund 70Germany

Personalised recommendations