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Differing perspectives of knowledge representation in artificial intelligence and discrete event modeling

  • Ashvin Radiya
  • Robert G. Sargent
Knowledge Representation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 444)

Abstract

In several subfields of Artificial Intelligence (AI) and in Discrete Event Modeling (DEM) there is a need to represent temporal and causal relationships in a problem domain. Some of these formalisms of AI and DEM are presented and compared. Most of the AI formalisms are beset by the frame, qualification, and/or ramification problems. DEM formalisms which can be viewed as formalisms for temporal and causal reasoning are not beset by these problems. They, however, in general, lack a formal theory. The Propositional Discrete Event Logic LPDE which avoids the characteristic problems of AI formalisms and which also gives a formal theory to DEM is briefly discussed. Examples illustrating the use of this logic are given.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1990

Authors and Affiliations

  • Ashvin Radiya
    • 1
  • Robert G. Sargent
    • 2
  1. 1.School of Computer and Information ScienceSyracuse UniversitySyracuseUSA
  2. 2.Simulation Research GroupSyracuse UniversitySyracuseUSA

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