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Journal of Artificial General Intelligence

The Journal of the Artificial General Intelligence Society

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Formalization of Evidence: A Comparative Study

Pei Wang
  • Temple University, Philadelphia, USA
Published Online: 2011-11-23 | DOI: https://doi.org/10.2478/v10229-011-0003-7

Formalization of Evidence: A Comparative Study

This article analyzes and compares several approaches of formalizing the notion of evidence in the context of general-purpose reasoning system. In each of these approaches, the notion of evidence is defined, and the evidence-based degree of belief is represented by a binary value, a number (such as a probability), or two numbers (such as an interval). The binary approaches provide simple ways to represent conclusive evidence, but cannot properly handle inconclusive evidence. The one-number approaches naturally represent inconclusive evidence as a degree of belief, but lack the information needed to revise this degree. It is argued that for systems opening to new evidence, each belief should at least have two numbers attached to indicate its evidential support. A few such approaches are discussed, including the approach used in NARS, which is designed according to the considerations of general-purpose intelligent systems, and provides novel solutions to several traditional problems on evidence.

Keywords: evidence; degree of belief; logic; probability; weight of evidence; revision; ignorance; evidential reasoning; general-purpose system

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About the article


Published Online: 2011-11-23

Published in Print: 2009-12-01



Citation Information: Journal of Artificial General Intelligence, ISSN (Online) 1946-0163, DOI: https://doi.org/10.2478/v10229-011-0003-7. Export Citation

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