A SELF-TRAINING SYSTEM THAT LEARNS THROUGH EXPERIMENTATION
DS 36: Proceedings DESIGN 2006, the 9th International Design Conference, Dubrovnik, Croatia
Year: 2006
Editor: Marjanovic, D.
Author: Braun, S.C.; Gero, J.S.
Section: METHODS AND TOOLS IN DESIGN PRACTICE
Page(s): 193-198
Abstract
The paper introduces an adaptive system that, inspired by the diversity of human cognitive development processes, uses different kinds of machine learning to develop its expertise. The system combines a supervised learning recognition engine with an autonomous learning agent. Based on the system’s initial knowledge novel training sets are produced through „experimental learning“. Thus, the user does not have to spend time on the generation of training data. Both the conceptual basis and an exemplary implementation (recognition and substitution of hand-drawn geometrical shapes) are presented. The dependency of the system’s learning success on the knowledge initially provided and the way it processes this knowledge is tested and documented.
Keywords: autonomous agents, adaptive tools, machine learning