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Published: December 8,2020

Physical world contains many complex sentient structures. They have evolved to learn how to organize themselves and optimally use the resources available to them while interacting with their environment. These complex adaptive systems (CAS) sustain their continued existence in the face of external forces causing large fluctuations. The study of CAS functions, structures and their dynamics under the influence of fluctuations has thrown light into self-organizing patterns that are common among these disparate systems. Common theme among these structures is that a system encodes and processes information to organize and manage its components interacting with each other and their environment. The self-organizing patterns also sense and counteract fluctuations to maintain their stability. They become autopoietic and maintain homeostasis. Digital Computing structures composed of distributed and communicating software and hardware components also fall into the category of a complex system where fluctuations in the demand for, or the availability of, resources required to execute the computa-tions disturb their stability and performance. The fluctuations impact the resilien-cy and efficiency of the structure as the scale of components increase. This paper describes the theory and practice of applying the self-organizing and self-managing patterns to distributed digital computing structures and making them autopoietic machines.

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Autopoietic Computing Systems and Triadic Automata: The Theory and Practice

**How to cite this paper:** Mark Burgin, Rao Mikkilineni, Vidya Phalke. (2020) Autopoietic Computing Systems and Triadic Automata: The Theory and Practice. *Advances in Computer and Communication*, **1**(**1**), 16-35.

DOI: http://dx.doi.org/10.26855/acc.2020.12.003

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