Transparency of execution using epigenetic networks

Dethlefs, Nina; Turner, Alexander

Abstract

This paper describes how the recurrent connectionist architecture epiNet, which is capable of dynamically modifying its topology, is able to provide a form of transparent execution. EpiNet, which is inspired by eukaryotic gene regulation in nature, is able to break its own architecture down into sets of smaller interacting networks. This allows for autonomous complex task decomposition, and by analysing these smaller interacting networks, it is possible to provide a real world understanding of why specific decisions have been made. We expect this work to be useful in fields where the risk of improper decision making is high, such as medical simulations, diagnostics and financial modelling. To test this hypothesis we apply epiNet to two data sets within UCI’s machine learning repository, each of which requires a specific set of behaviours to solve. We then perform analysis on the overall functionality of epiNet in order to deduce the underlying rules behind its functionality and in turn provide transparency of execution.

Citation

Dethlefs, N., & Turner, A. (2017). Transparency of execution using epigenetic networks. In C. Knibbe, G. Beslon, D. Parsons, D. Misevic, J. Rouzaud-Cornabas, N. Bredeche, …H. Soula (Eds.), Proceedings of the ECAL 2017 (404-411). https://doi.org/10.7551/ecal_a_068

• Conference Name: The European Conference on Artificial Life 2017
• Conference Location: Lyon, France
• Start Date: Sep 4, 2017
• End Date: Sep 8, 2017
• Acceptance Date: Sep 5, 2017
• Publication Date: 2017-09
• Deposit Date: Sep 25, 2017
• Journal: The European Conference on Artificial Life 2017
• Publisher: Massachusetts Institute of Technology Press
• Peer Reviewed: Not Peer Reviewed
• Volume: 14
• Pages: 404-411
• Book Title: Proceedings of the ECAL 2017
• ISBN: 978-0-262-34633-7
• DOI: https://doi.org/10.7551/ecal_a_068
• Keywords: Transparency; Artificial intelligence; Topological Morphology
• Public URL: https://hull-repository.worktribe.com/output/455037
• Publisher URL: http://cognet.mit.edu/proceed/10.7551/ecal_a_068