A framework for heuristic reasoning about uncertainty

  • Authors:
  • Paul R. Cohen;Milton R. Grinberg

  • Affiliations:
  • Department of Computer Science, Stanford University, Stanford, CA;Department of Computer Science, Stanford University, Stanford, CA

  • Venue:
  • IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
  • Year:
  • 1983

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper describes a theory of reasoning about uncertainly, based on a representation of state'i of certainly called endorsements (see Cohen and Grinberg, 1983, for a more detailed discussion of the theory.) The theory of endorsements is an alternative to numerical methods for reasoning about uncertainty, such as subjective Bayesian methods (Shortliffe and Buchanan, 1975; Duda, Hart, and Nilsson, 1976) and the Shafer-Dempster theory (Shafer, 1976). The fundamental concern with numerical representations of certainty is that they hide the reasoning that produces them and thus limit one's reasoning about uncertainty. While numbers are easy to propagate over inferences, what the numbers mean is unclear. The theory of endorsements represents the factors that affect certainty and supports multiple strategies for dealing with uncertainty.