Modeling and verification of the "cause &effect" relation comprehension by neural networks using learning algorithms

Author

Muquit, M.A. ; Sawada, Yasuji

Author_Institution

Graduate Sch. of Inf. Sci., Tohoku Univ., Sendai, Japan

Volume

1

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

939

Lastpage

943

Abstract

The capability of predicting what will happen next based on past experiences and environment features could be thought of as one kind of cause & effect comprehension. We have modeled a system to attain this capability by neural networks using reinforcement and supervised learning algorithms to comprehend the ground shot in a golf game. The system predicts the approximate distance the golf ball runs and we have found that the system works very well and it becomes almost perfect just after practicing with several samples

Keywords

learning (artificial intelligence); neural nets; bias knowledge; cause and effect relation comprehension; ground shot; learning algorithms; neural networks; prediction; reinforcement learning; supervised learning; Biological neural networks; Biology computing; Humans; Information science; Machine learning; Neural networks; Prediction algorithms; Predictive models; Supervised learning; Watches;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1005601

Filename

1005601