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A computational framework for autonomous self-repair systems

Minh-Thai, TN, Aryal, J ORCID: 0000-0002-4875-2127, Samarasinghe, J and Levin, M 2018 , 'A computational framework for autonomous self-repair systems', in T Mitrovic and B Xue and X Li (eds.), Proceedings of the 31st Australasian Joint Conference on Artificial Intelligence (AI 2018) , Springer, Switzerland, pp. 1-6 , doi: 10.1007/978-3-030-03991-2.

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Abstract

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 Type: Conference Publication
Authors/Creators:Minh-Thai, TN and Aryal, J and Samarasinghe, J and Levin, M
Keywords: signalling entropy, modeling, perceptron, perturbation, noise
Journal or Publication Title: Proceedings of the 31st Australasian Joint Conference on Artificial Intelligence (AI 2018)
Publisher: Springer
DOI / ID Number: 10.1007/978-3-030-03991-2
Copyright Information:

Copyright 2018 Springer Nature Switzerland AG

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