A knowledge-based genetic heuristic for learning certainty factors

Author

Lynch, Douglas B. ; Kuncicky, David C.

Author_Institution

Dept. of Comput. Sci., Florida State Univ., Tallahassee, FL, USA

fYear

1994

fDate

27-29 Jun 1994

Firstpage

125

Abstract

An expert network is a type of inference network that is derived from an expert system. One of the uses of expert networks is to to refine measures of certainty in knowledge bases using neural network learning techniques. Goal-directed Monte Carlo search (GDMC) is a parallel stochastic hillclimbing method that is being successfully used to refine certainty factors from data. This paper presents a new heuristic for GDMC that improves its performance by incorporating genetic algorithm techniques

Keywords

expert systems; genetic algorithms; heuristic programming; inference mechanisms; learning (artificial intelligence); neural nets; Goal-directed Monte Carlo search; certainty factors; expert network; genetic algorithm; genetic heuristic; heuristic; inference network; knowledge bases; knowledge-based; learning certainty factors; learning techniques; neural network; parallel stochastic hillclimbing method; Computer science; Electronic mail; Error correction; Expert systems; Feedforward neural networks; Feeds; Genetics; Monte Carlo methods; Neural networks; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1899-4

Type

conf

DOI

10.1109/ICEC.1994.350029

Filename

350029