---
title: "Learning to Count by Think Aloud Imitation"
authors:
  - "Laurent Orseau"
doi: ""
url: "https://ijcai.org/proceedings/2007/162"
pdf: "https://ijcai.org/proceedings/2007/0162.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "1005-1009"
track: ""
keywords:
---

# Learning to Count by Think Aloud Imitation

Laurent Orseau

*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.