Abstract
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.
Highlights
Data are objects known or assumed as facts, forming the basis of reasoning or calculation
The important innovation here theoretical is the regulatory overlaycomputational to discover/configure, monitor, In this paper, we describe and practical tools for working and manage the traditional process evolution using the local how with knowledge structures,computing such as schemas, taking into account theirknowledge inter- andofintrathe local IaaS and PaaS are configured, monitored, and managed while coordinating with global knowledge to optimize the end-to-end system behavior in the face of fluctuations
The knowledge structures represent this schema in the form of a system of named sets containing various data elements, objects, or entities that are composed of the data, and their inter-object and intra-object relationships and behaviors associated with events that cause changes to the instances as time progresses [27]
Summary
Data are objects known or assumed as facts, forming the basis of reasoning or calculation. Schemas are studied and used in a variety of areas including neurophysiology, psychology, computer science, Internet technology, databases, logic, and mathematics The reason for such high-level development and application is the existence of different types of schemas (in brain theory, cognitive psychology, artificial intelligence, programming, networks, computer science, mathematics, databases, etc.). To better understand human intellectual and practical activity (thinking, decisionmaking, and learning) and to build artificial intelligence, we have to be able to work with a variety of schema types Such opportunities are provided by the mathematical schema theory developed by one of the authors [23,24,25], elements of which are presented in Section 2 of this paper. Comput. 2021, 5, 13 of flexible theoretical tools for the study and advancement of existing computing and network systems
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