Learning by Cooperating Agents
Raj Bhatnagar · 1997
Most algorithms for learning and pattern discovery in data assume that all the needed data is available on one computer at a single site. This assumption does not hold in situations where a number of inde-pendent databases reside on different nodes of a net-work. These databases cannot be moved to a common shared site due to size, security, privacy, legal, and data-ownership concerns but all of them together con-stitute the dataset in which patterns must be discov-ered. These databases, however, may be made acces-sible for certain types of queries and all such commu-nications for a database may be channeled through an intelligent agent interface. In this paper we show how a decision-tree induction algorithm may be adapted for such situations and implemented in terms of com-munications among the interface agents.