A learning model for the selection of problem solving strategies in continuous physical systems

Author

Xia, Xiaodong ; Yeung, Dit-Yan

Author_Institution

Dept. of Comput. Sci., Univ. of Southern California, Los Angeles, CA, USA

fYear

1988

fDate

14-18 Mar 1988

Firstpage

200

Lastpage

206

Abstract

The authors present a learning model for automatic selection of problem-solving strategies in a physical system working in a continuous, dynamic environment. The model is intended to provide (1) a means to classify under what circumstances one problem-solving strategy (goal-directed or data-driven) is better than another; and (2) a simple online learning mechanism for obtaining a better classification when the strategy chosen based on the initial one does not yield the desired performance. The ideas and applicability of the model are illustrated by an example system for robotic assembly which has been under implementation

Keywords

expert systems; learning systems; classification; continuous physical systems; learning model; online learning mechanism; problem solving strategies; robotic assembly; Artificial intelligence; Assembly systems; Computer science; Contracts; Control systems; Data analysis; Learning systems; Problem-solving; Robotic assembly; Robotics and automation;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Intelligence Applications, 1988., Proceedings of the Fourth Conference on

Conference_Location

San Diego, CA

Print_ISBN

0-8186-0837-4

Type

conf

DOI

10.1109/CAIA.1988.196104

Filename

196104