Title :
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
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;
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
DOI :
10.1109/ICEC.1994.350029
