In this paper we describe an Artificial Gene Regulatory Network (AGRN), whose form and function are inspired by biological epigenetics. This new architecture, termed an Artificial Epigenetic Network (AEN), is applied to the coupled inverted pendulum task, a control task that has complex non-linear dynamics. The AENs show significant benefits over previous AGRNs. Firstly, when applied to the coupled inverted pendulum task, they show a significant performance increase. In addition, the AENs self-partition, applying different genes to control different dynamics within the task, which is more analogous to gene regulation in nature. These networks also make it possible to gain user control over the dynamics of the network via the modification of the epigenetic layer.
@inproceedings(SS-SSCI13, author = "Alexander P. Turner and Michael A. Lones and Luis A. Fuente and Susan Stepney and Leo S. D. Caves and Andy Tyrrell", title = "The Artificial Epigenetic Network", pages = "66-72", crossref = "SSCI13" ) @proceedings(SSCI13, title = "SSCI13 2013, Singapore, April 2013", booktitle = "SSCI13 2013, Singapore, April 2013", publisher = "IEEE Press", year = 2013 )