An organized society of autonomous knowledge discovery agents

9Citations
Citations of this article
2Readers
Mendeley users who have this article in their library.
Get full text

Abstract

We have been developing a methodology and system for autonomous knowledge discovery and data mining from global information sources. The key issue is how to increase both autonomy and versatility of our discovery system. Our methodology is to create an organized society of autonomous knowledge discovery agents. This means (l) to develop many kinds of knowledge discovery and data mining agents (KDD agents in short) for different objects; (2) to use the KDD agents in multiple learning phases in a distributed cooperative mode; (3) to manage the society of the KDD agents by multiple meta-control levels. Based on this methodology, a multi-strategy and cooperative discovery system, which can be imagined as a softbot and is named GLS (Global Learning Scheme), has being developing by us. This paper briefly describes our methodology and the framework of our GLS system.

Cite

CITATION STYLE

APA

Zhong, N., Kakemoto, Y., & Ohsuga, S. (1997). An organized society of autonomous knowledge discovery agents. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1202, pp. 184–194). Springer Verlag. https://doi.org/10.1007/3-540-62591-7_33

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free