Learning to Count by Think Aloud Imitation
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 1005-1009.
Abstract
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.
@inproceedings{ijcai2007p162,
title = {Learning to Count by Think Aloud Imitation},
author = {Orseau, Laurent},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {1005--1009},
year = {2007},
url = {https://ijcai.org/proceedings/2007/162},
}
TY - CPAPER TI - Learning to Count by Think Aloud Imitation AU - Orseau, Laurent T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 1005 EP - 1009 UR - https://ijcai.org/proceedings/2007/162 ER -Copied
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