White Rose University Consortium logo
University of Leeds logo University of Sheffield logo York University logo

Spiky RBN: A Sub-symbolic Artificial Chemistry

Krasetv, Mihail Svilenov (2018) Spiky RBN: A Sub-symbolic Artificial Chemistry. PhD thesis, University of York.

This is the latest version of this item.

[img]
Preview
Text
SpikyRBN_SubSymbolicAchem.pdf - Examined Thesis (PDF)
Available under License Creative Commons Attribution-Noncommercial-No Derivative Works 2.0 UK: England & Wales.

Download (3400Kb) | Preview

Abstract

We design and build a sub-symbolic artificial chemistry based on random boolean networks (RBN). We show the expressive richness of the RBN in terms of system design and the behavioural range of the overall system. This is done by first generating reference sets of RBNs and then comparing their behaviour as we add mass conservation and energetics to the system. The comparison is facilitated by an activity measure based on information theory and reaction graphs but tailored for our system. The system is used to reason about methods of designing complex systems and directing them towards specific tasks.

Item Type: Thesis (PhD)
Academic Units: The University of York > Computer Science (York)
Identification Number/EthosID: uk.bl.ethos.778912
Depositing User: Mr Mihail Svilenov Krasetv
Date Deposited: 04 Jun 2019 13:40
Last Modified: 19 Feb 2020 13:08
URI: http://etheses.whiterose.ac.uk/id/eprint/24115

Available Versions of this Item

  • Spiky RBN: A Sub-symbolic Artificial Chemistry. (deposited 04 Jun 2019 13:40) [Currently Displayed]

You do not need to contact us to get a copy of this thesis. Please use the 'Download' link(s) above to get a copy.
You can contact us about this thesis. If you need to make a general enquiry, please see the Contact us page.

Actions (repository staff only: login required)