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.
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References
Arbuckle, D.J., Requicha, A.A.G.: Self-assembly and self-repair of arbitrary shapes by a swarm of reactive robots: algorithms and simulations. Auton. Robots 28(2), 197–211 (2010)
Bessonov, N., Levin, M., Morozova, N., Reinberg, N., Tosenberger, A., Volpert, V.: On a model of pattern regeneration based on cell memory. PLOS ONE 10(2), e0118091 (2015)
De, A., Chakravarthy, V.S., Levin, M.: A computational model of planarian regeneration. Int. J. Parallel Emerg. Distrib. Syst. 32(4), 331–347 (2017)
Dinsmore, C.E.: A history of regeneration research: milestones in the evolution of a science. J. Hist. Biol. 26(1), 156–158 (1993)
Edwards, C.: Self-repair techniques point to robots that design themselves. Commun. ACM 59(2), 15–17 (2016)
Ferreira, G.B.S., Smiley, M., Scheutz, M., Levin, M.: Dynamic structure discovery and repair for 3D cell assemblages. In: Proceedings of the Fifteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFEXV) (2016)
Frei, R., McWilliam, R., Derrick, B., Purvis, A., Tiwari, A., Di Marzo Serugendo, G.: Self-healing and self-repairing technologies. Int. J. Adv. Manuf. Technol. 69(5), 1033–1061 (2013)
Gerlee, P., Basanta, D., Anderson, A.R.A.: Evolving homeostatic tissue using genetic algorithms. Prog. Biophys. Mole. Biol. 106(2), 414–425 (2011)
Levin, M.: The wisdom of the body: future techniques and approaches to morphogenetic fields in regenerative medicine, developmental biology and cancer. Regen. Med. 6(6), 667–673 (2011). pMID: 22050517
Levin, M.: Morphogenetic fields in embryogenesis, regeneration, and cancer: non-local control of complex patterning. Biosystems 109(3), 243–261 (2012). Biological Morphogenesis: Theory and Computation
Lobo, D., Solano, M., Bubenik, G.A., Levin, M.: A linear-encoding model explains the variability of the target morphology in regeneration. J. Roy. Soc. Interface 11(92), 20130918 (2014)
Mustard, J., Levin, M.: Bioelectrical mechanisms for programming growth and form: taming physiological networks for soft body robotics. Soft Robot. 1(3), 169–191 (2014). Biological Morphogenesis: Theory and Computation
Rubenstein, M., Sai, Y., Chuong, C., Shen, W.: Regenerative patterning in swarm robots: mutual benefits of research in robotics and stem cell biology. Int. J. Dev. Biol. 53(5–6), 869–881 (2009)
Teschendorff, A.E., Enver, T.: Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome. Nat. Commun. 8, 15599 (2017)
Tosenberger, A., Bessonov, N., Levin, M., Reinberg, N., Volpert, V., Morozova, N.: A conceptual model of morphogenesis and regeneration. Acta Biotheor. 63(3), 283–294 (2015)
Acknowledgments
Authors gratefully acknowledge support of the the following: TNM - Doctoral Scholarship from VIED, Vietnam; J.A.- Sabbatical at Lincoln University, New Zealand; S.S.- Lincoln University Research Fund; M.L.- DARPA (#HR0011-18-2-0022), the Allen Discovery Center award from the Paul G Allen Frontiers Group, and the Templeton World Charity Foundation (TWCF0089/AB55 and TWCF0140).
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Minh-Thai, T.N., Aryal, J., Samarasinghe, S., Levin, M. (2018). A Computational Framework for Autonomous Self-repair Systems. In: Mitrovic, T., Xue, B., Li, X. (eds) AI 2018: Advances in Artificial Intelligence. AI 2018. Lecture Notes in Computer Science(), vol 11320. Springer, Cham. https://doi.org/10.1007/978-3-030-03991-2_16
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DOI: https://doi.org/10.1007/978-3-030-03991-2_16
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