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

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.

Hidden Gold for IT Professionals, Educators, and Students: Insights From Stack Overflow Survey

“…Data can be information processed or stored by a computer or facts used as a basis for reasoning and computation, which can appear in textual or visual forms [33], [34]. The information may be in the form of text documents, images, audio clips, software programs, or other types of data.…”

Research on the differential discrimination of electric power standards based on knowledge graph

“…Knowledge processing mainly refers to knowledge reasoning, which can be used to complement the knowledge graph [8] . Approaches of knowledge reasoning can be divided into two categories, one is based on the deduction, and the other is based on induction.…”

The Science of Information Processing Structures and the Design of a New Class of Distributed Computing Structures

“…Each transaction specifies the operations on the knowledge structure representing the application and its behavior as a graph (nodes, links, and their relationships and behaviors). Thus, the operation on the knowledge structures is independent of the content and solely manages the structure by reconfiguring the knowledge nodes as a network of networks [10]. All nodes that are wired together fire together to exhibit the intended behavior.…”

“…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.…”