Statistical Science

Probability Judgment in Artificial Intelligence and Expert Systems

Glenn Shafer

Full-text: Open access

Abstract

Historically, the study of artificial intelligence has emphasized symbolic rather than numerical computation. In recent years, however, the practical needs of expert systems have led to an interest in the use of numbers to encode partial confidence. There has been some effort to square the use of these numbers with Bayesian probability ideas, but in most applications not all the inputs required by Bayesian probability analyses are available. This difficulty has led to widespread interest in belief functions, which use probability in a looser way. It must be recognized, however, that even belief functions require more structure than is provided by pure production systems. The need for such structure is inherent in the nature of probability argument and cannot be evaded. Probability argument requires design as well as numerical inputs. The real challenge probability poses to artificial intelligence is to build systems that can design probability arguments. The real challenge artificial intelligence poses to statistics is to explain how statisticians design probability arguments.

Article information

Source
Statist. Sci., Volume 2, Number 1 (1987), 3-16.

Dates
First available in Project Euclid: 19 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.ss/1177013426

Digital Object Identifier
doi:10.1214/ss/1177013426

Mathematical Reviews number (MathSciNet)
MR896256

Zentralblatt MATH identifier
0955.68506

JSTOR
links.jstor.org

Keywords
Artificial intelligence associative memory Bayesian networks belief functions certainty factors conditional independence constructive probability diagnostic trees expert systems production systems

Citation

Shafer, Glenn. Probability Judgment in Artificial Intelligence and Expert Systems. Statist. Sci. 2 (1987), no. 1, 3--16. doi:10.1214/ss/1177013426. https://projecteuclid.org/euclid.ss/1177013426


Export citation