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doi:10.1016/0888-613X(88)90118-1    
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Copyright © 1988 Published by Elsevier Science Inc.

Choosing uncertainty representations in artificial intelligence

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Tod S. LevittCorresponding Author Contact Information

Advanced Decision Systems, USA


Available online 20 May 2003.

Abstract

Research in automation in machine vision, robotic planning, medical diagnosis, and many other fields gives rise to sources of uncertainty in inference and reasoning that are beyond conventional notions of measurement error. For a given domain of interest, an uncertainty representation is defined such that statements in the representation language model states of the domain and can provide problem solutions. Five criteria are proposed for selecting uncertainty representations for artificial intelligence: mathematical soundness, efficacy of domain models, appropriate representation languages, efficient computation, and choice of control mechanism. These issues are explored in an application of determining a building's location from multiple sensor returns. Finally, choice of uncertainty representations is seen to depend on choices for uncertain inference and control.

Author Keywords: uncertain reasoning; certainty calculus; uncertainty representation; artificial intelligence; probability; belief functions; possibility theory

Article Outline

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Corresponding Author Contact InformationCorresponding author. Address correspondence to Todd Levitt, ADS, 1500 Plymouth Street, Mountain View, California 94043-1230.


 
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