Autopoietic Computing Systems and Triadic Automata: The Theory and Practice

Burgin, Mark; Mikkilineni, Rao; Phalke, Vidya

doi:10.26855/acc.2020.12.003

Cited by 14 publications

(28 citation statements)

References 19 publications

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“…The infware, i.e., the processed information in the form of data structures, uses the operations on the schema representing the hardware, software, and their evolutionary behaviors as dynamic knowledge structures. An autopoietic system [32,33] implemented using triadic structural machines, i.e., structural machines working as triadic automata, is capable "of regenerating, reproducing, and maintaining itself by production, transformation, and destruction of its components and the networks of processes downstream contained in them." The autopoietic machines discussed in this paper, which operate on schema containing knowledge structures, allow us to deploy and manage non-stop and highly reliable computing structures at scale independent of whose hardware and software are used.…”

Section: Computing Structures For Operation With Schemasmentioning

confidence: 99%

From data Processing to Knowledge Processing: Working with Operational Schemas by Autopoietic Machines

Burgin

Mikkilineni

2021

BDCC

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Knowledge processing is an important feature of intelligence in general and artificial intelligence in particular. To develop computing systems working with knowledge, it is necessary to elaborate the means of working with knowledge representations (as opposed to data), because knowledge is an abstract structure. There are different forms of knowledge representations derived from data. One of the basic forms is called a schema, which can belong to one of three classes: operational, descriptive, and representation schemas. The goal of this paper is the development of theoretical and practical tools for processing operational schemas. To achieve this goal, we use schema representations elaborated in the mathematical theory of schemas and use structural machines as a powerful theoretical tool for modeling parallel and concurrent computational processes. We describe the schema of autopoietic machines as physical realizations of structural machines. An autopoietic machine is a technical system capable of regenerating, reproducing, and maintaining itself by production, transformation, and destruction of its components and the networks of processes downstream contained in them. We present the theory and practice of designing and implementing autopoietic machines as information processing structures integrating both symbolic computing and neural networks. Autopoietic machines use knowledge structures containing the behavioral evolution of the system and its interactions with the environment to maintain stability by counteracting fluctuations.

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Section: Computing Structures For Operation With Schemasmentioning

confidence: 99%

From data Processing to Knowledge Processing: Working with Operational Schemas by Autopoietic Machines

Burgin

Mikkilineni

2021

BDCC

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“…The structural machines supersede the Turing machines by their representations of knowledge and the operations that process information [20]. Triadic structural machines with multiple general and mission-oriented processors enable autopoietic behaviors.…”

Section: General Theory Of Information and The Burgin-mikkilineni The...mentioning

confidence: 99%

“…All the knowledge nodes wired together fire together to execute a collective behavior. The knowledge network is implemented as a structural machine that provides operations on the Knowledge structure schema [20,21] A design of the implementation of a structural machine using knowledge structures that represent the "life processes" of a computational workflow application is shown in figure 5. The digital genome is like a cell that contains all the executables, their control structures and operational details about an application designed to be a distributed computing structure executed in infrastructures offered by different providers.…”

Section: Infusing Autopoietic and Cognitive Behaviors Into Digital Au...mentioning

confidence: 99%

A New Class of Autopoietic and Cognitive Machines

Mikkilineni¹

2021

Preprint

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Making computing machines mimic living organisms has captured the imagination of many since the dawn of digital computers. However, today’s artificial intelligence technologies fall short in replicating even the basic autopoietic and cognitive behaviors found in primitive biological systems. According Charles Darwin, the difference in mind between humans and the higher animals, great as it is, certainly is one of degree and not of kind. Autopoiesis refers to the behavior of a system that replicates itself and maintains its own identity and stability while facing fluctuations caused by external influences. Cognitive behaviors model the system’s state, sense internal and external changes, analyze, predict and take action to mitigate any risk to its functional fulfilment. How did intelligence evolve? what is the relationship between the mind and body? Answers to these questions should guide us to infuse autopoietic and cognitive behaviors into digital machines. In this paper we use recent advances in our understanding of general theory of information, and the role of structures in managing the transformations between information and knowledge to pave the path to infuse autopoietic and cognitive functions into digital computing and build a new class of intelligent machines going beyond the current state of the art.

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“…In Section 4, we discuss how autopoiesis and cognition work utilizing symbolic computing and sub-symbolic computing. In Section 5, we discuss the design of digital automata, called autopoietic machines [8][9][10], with autopoietic and cognitive behaviors using digital genes and digital neurons. In Section 6, we conclude with some observations on current attempts to implement autopoietic machines.…”

Section: Introductionmentioning

confidence: 99%