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A computational framework for autonomous self-repair systems


Minh-Thai, TN and Aryal, J and Samarasinghe, J and Levin, M, A computational framework for autonomous self-repair systems, Proceedings of the 31st Australasian Joint Conference on Artificial Intelligence (AI 2018), 11-14 December 2018, Wellington, New Zealand, pp. 1-6. ISBN 978-3-030-03991-2 (2018) [Refereed Conference Paper]

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Copyright 2018 Springer Nature Switzerland AG

DOI: doi:10.1007/978-3-030-03991-2


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.

Item Details

Item Type:Refereed Conference Paper
Keywords:signalling entropy, modeling, perceptron, perturbation, noise
Research Division:Engineering
Research Group:Geomatic engineering
Research Field:Photogrammetry and remote sensing
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the mathematical sciences
UTAS Author:Aryal, J (Dr Jagannath Aryal)
ID Code:129037
Year Published:2018
Deposited By:Geography and Spatial Science
Deposited On:2018-11-05
Last Modified:2019-03-25

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