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arXiv · 1903.09297

Transparent Machine Education of Neural Networks for Swarm Shepherding Using Curriculum Design

Alexander Gee·Hussein Abbass
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Abstract

Swarm control is a difficult problem due to the need to guide a large number of agents simultaneously. We cast the problem as a shepherding problem, similar to biological dogs guiding a group of sheep towards a goal. The shepherd needs to deal with complex and dynamic environments and make decisions in order to direct the swarm from one location to another. In this paper, we design a novel curriculum to teach an artificial intelligence empowered agent to shepherd in the presence of the large state space associated with the shepherding problem and in a transparent manner. The results show that a properly designed curriculum could indeed enhance the speed of learning and the complexity of learnt behaviours.

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Alexander Gee, Hussein Abbass. 2019-01-04. Transparent Machine Education of Neural Networks for Swarm Shepherding Using Curriculum Design. https://arxiv.org/abs/1903.09297

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