A computational framework for autonomous self-repair systems
conference contribution
posted on 2023-05-23, 13:45authored byMinh-Thai, TN, Jagannath Aryal, Samarasinghe, J, Levin, M
This paper describes a novel computational framework for damage detection and regeneration in an artificial tissue of cells resembling living systems.We represent the tissue as an Auto-Associative Neural Network (AANN) consisting of a single layer of perceptron neurons (cells) with local feedback loops. This allows the system to recognise its state and geometry in a form of collective intelligence. Signalling entropy is used as a global (emergent) property characterising the state of the system. The repair system has two submodels - global sensing and local sensing. Global sensing is used to sense the change in whole system state and detect general damage region based on system entropy change. Then, local sensing is applied with AANN to find the exact damage locations and repair the damage. The results show that the method allows robust and efficient damage detection and accurate regeneration.
History
Publication title
Proceedings of the 31st Australasian Joint Conference on Artificial Intelligence (AI 2018)
Editors
T Mitrovic, B Xue, X Li
Pagination
1-6
ISBN
978-3-030-03991-2
Department/School
School of Geography, Planning and Spatial Sciences
Publisher
Springer
Place of publication
Switzerland
Event title
31st Australasian Joint Conference on Artificial Intelligence (AI 2018)
Event Venue
Wellington, New Zealand
Date of Event (Start Date)
2018-12-11
Date of Event (End Date)
2018-12-14
Rights statement
Copyright 2018 Springer Nature Switzerland AG
Repository Status
Restricted
Socio-economic Objectives
Diagnosis of human diseases and conditions; Expanding knowledge in the mathematical sciences