Learning to Count by Think Aloud Imitation

Laurent Orseau

Although necessary, learning to discover new solutions is often long and difficult, even for supposedly simple tasks such as counting. On the other hand, learning by imitation provides a simple way to acquire knowledge by watching other agents do. In order to learn more complex tasks by imitation than mere sequences of actions, a Think Aloud protocol is introduced, with a new neuro-symbolic network. The latter uses time in the same way as in a Time Delay Neural Network, and is added basic first order logic capacities. Tested on a benchmark counting task, learning is very fast, generalization is accurate, whereas there is no initial bias toward counting.

Subjects: 12. Machine Learning and Discovery; 14. Neural Networks

Submitted: Oct 13, 2006

This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.