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Integrating uncertainty handling formalisms in distributed artificial intelligence

  • Simon Parsons
  • Alessandro Saffiotti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 747)

Abstract

In distributed artificial intelligence systems it is important that the constituent intelligent systems communicate. This may be a problem if the systems use different methods to represent uncertain information. This paper presents a method that enables systems that use different uncertainty handling formalisms to qualitatively integrate their uncertain information, and argues that this makes it possible for distributed intelligent systems to achieve tasks that would otherwise be beyond them.

Keywords

Belief Function Uncertain Information Monotonicity Assumption Artificial Intelligence System Conditional Belief 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Simon Parsons
    • 1
    • 2
  • Alessandro Saffiotti
    • 3
  1. 1.Advanced Computation LaboratoryImperial Cancer Research FundLondonUK
  2. 2.Department of Electronic EngineeringQueen Mary and Westfield CollegeLondon
  3. 3.IRIDIAUniversité Libre de BruxellesBruxellesBelgium

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