Knowledge-Intensive Recruitment Learning

TitleKnowledge-Intensive Recruitment Learning
Publication TypeTechnical Report
Year of Publication1988
AuthorsDiederich, J.
Other Numbers496

The model described in this paper is a knowledge-intensive connectionist learning system which uses a built-in knowledge representation module for inferencing, and this reasoning capability in turn is used for knowledge-intensive learning. On the connectionist network level, the central process is the recruitment of new units and the assembly of units to represent new conceptual information. Free, uncommitted subnetworks are connected to the built-in knowledge network during learning. The goal of knowledge-intensive connectionist learning is to improve the operationality of the knowledge representation: mediated inferences, i.e., complex inferences which require several inference steps, are transformed into immediate inferences; in other words, recognition is based on the immediate excitation from features directly associated with a concept.

Bibliographic Notes

ICSI Technical Report TR-88-010

Abbreviated Authors

J. Diederich

ICSI Publication Type

Technical Report