Machine Education: Designing semantically ordered and ontologically guided modular neural networks
Abstract
The literature on machine teaching, machine education, and curriculum design for machines is in its infancy with sparse papers on the topic primarily focusing on data and model engineering factors to improve machine learning. In this paper, we first discuss selected attempts to date on machine teaching and education. We then bring theories and methodologies together from human education to structure and mathematically define the core problems in lesson design for machine education and the modelling approaches required to support the steps for machine education. Last, but not least, we offer an ontology-based methodology to guide the development of lesson plans to produce transparent and explainable modular learning machines, including neural networks.
Cite
@article{arxiv.2002.03841,
title = {Machine Education: Designing semantically ordered and ontologically guided modular neural networks},
author = {Hussein A. Abbass and Sondoss Elsawah and Eleni Petraki and Robert Hunjet},
journal= {arXiv preprint arXiv:2002.03841},
year = {2020}
}
Comments
IEEE Symposium Series on Computational Intelligence, 2019