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Cybernetics and transport processes automation. Tutorial
Alexander Korpukov
Vadim Shmal
Pavel Minakov
Dmitry Abramov
Abramov Dmitry, Moscow Polytechnic UniversityKorpukov Alexander, Pirogov Russian National Research Medical UniversityShmal Vadim, Federal state autonomous educational institution of higher education «Russian university of transport»Minakov Pavel, Federal state autonomous educational institution of higher education «Russian university of transport»
Cybernetics and transport processes automation
Tutorial
Alexander Korpukov
Dmitry Abramov
Vadim Shmal
Pavel Minakov
© Alexander Korpukov, 2022
© Dmitry Abramov, 2022
© Vadim Shmal, 2022
© Pavel Minakov, 2022
ISBN 978-5-0059-3941-8
Created with Ridero smart publishing system
About cybernetics
Cybernetics is the science of communication and control. He also explores the self-perception of people and social groups. This concerns how human activities and communication affect collective behavior. The social context of cybernetics is vast and growing. Cybernetics is a dynamic and diverse field of research. New trends and scientific discoveries will influence the field of cybernetics in the coming years.
Cybernetics is a portfolio word that combines cybernetic with biology. American mathematician John von Neumann published Automata, an article on cybernetics, in which he outlined the fundamental paradigm of the theory: there are situations that are controlled by a central computer. Von Neumann applied the term «automaton» to any device or system that «can be analyzed like automata».
Cybernetics takes a holistic approach and works with communication at an elementary level. Early cybernetics also explored how language affects the way people interact. Topics of how society and people perceive and interact with information technology are of great interest. A special issue of «Cybernetics» examines the meaning and development of the word «cybernetics». These reviews shed light on this rather little-known branch of science. Despite these theoretical advances and new developments, this area is still poorly understood. Only 10% to 30% of researchers working in this field publish more than three articles a year. A 2006 study found that there is a dead end in attracting the attention of leading journals to new research proposals.
The applied part of cybernetics deals with the control and movement of systems, as well as how to regulate or control their behavior. Along with systems theory, statistics, and operations research, cybernetics is one of the three main disciplines of science and technology and the first scientific discipline to deal with controlling and influencing the behavior of a system. The main goal of the broad field of cybernetics is to understand and define human intelligence. According to cybernetics, the process of understanding how to build and maintain the human brain and its intellectual capacity is complex and multidimensional.
Cybernetics is defined as the study of interactions between people and things, the study of the interaction between people and their environment, the study of systems, the systematization of actions. The importance of understanding these interrelationships is what made cybernetics one of the most widespread sciences in the 20th century. The scientific study of any human phenomenon – action, planning, protection, communication, etc. – was included in the disciplinary study of cybernetics.
Cybernetics has been defined in different ways, by different people, from a wide variety of disciplines. It is a broad concept that encompasses many areas. On one level, it has to do with the nature of all life; transfer and control of information within biological systems and between them. On the other hand, we are talking about the control of processes at the atomic and molecular levels and the network connections between them.
Research automation proves that a key innovation in machine intelligence is achieving or exceeding the ability of humans to control and manipulate data. The fundamental role of a computer (or smart machine) is not to make calculations; but manage the information processed by the machine. The information network is the basis of intelligence. AI’s primary focus is to develop systems that can monitor the network and dynamically change its connections to improve its performance in response to changing circumstances.
The «correlation versus causality» discussion in cybernetics means that we need to interpret the data without succumbing to Cartesian dualism. In terms of neoclassical economics, the main driving forces of business are the subjective preferences of people, driven by incentives. The emergent point of view is an emergent system in which different levels of causal structure appear and disappear over time. Bostrom uses this model to see the nature of intelligence.
Robots and other artificial intelligence systems must evolve following a strategy of making the system as responsive to the environment as possible. They must constantly adapt and improve, following the rules given to them, the strategy adopted for this reason, because a human programmer cannot foresee all future events. The rule-based nature of AI is a key ingredient in its evolution and also, in other words, its goal (although this goal is often overlooked). The ability to learn from experience (learning by doing) is fundamental to intelligent behavior.
The development of AI led by humans will not be associated with the construction of a high-performance «superintelligence»; but about strengthening and expanding the system in relation to those fundamental principles of cybernetics that we expect from people: learning, adaptation and repetition. A certain «learning to learn» (programmability, emergent behavior) is the foundation of cybernetics.
Once the AI is created, the system must evolve like any other living system; learning to adapt to the environment as it develops through natural selection, kind of like a Darwinian process. The emulation (evaluation) process is critical to what happens in AI. We can simulate an AI system by simulating a problem. We did this by simulating a chess program. However, the result is limited. He is only able to reproduce simple chess-related activities. This is possible because we have limited the number of things the system can do. We have only simulated the output of the program.
It is impossible to create a robot unless we first understand the basic process by which the system learns, building it, based on trial and error. To learn, a system must understand what it is doing and have some ability to reverse the processes it is learning. The process of developing an AI system should be copying a simpler system with its own rules.
Since we do not design «upgrades» to our artificial intelligence systems, they evolve by copying some simpler system. The adaptive system does not repeat a fixed sequence of events in order to learn; rather, it needs to learn about different patterns, behaviors and habits. This imitation process is based on a stimulus-response function.
The principle of adaptive learning (or learning by doing) is a good example of imitation in action. It is the process by which any machine, any computer or intelligent agent learns how it should behave based on its experience. Learning by imitation is similar to this principle, but it is based on the fact that a person (or group of people) imitates another group or person in order to learn something new. The emulated group or person has their own individual rules of operation (rules of imitation) that determine what types of reactions or behaviors are learned.
Adaptive emulators (learning by emulation) play a crucial role in the development of intelligence. This is the most important mechanism for learning and developing knowledge. According to Bostrom, they will also play a crucial role in the evolution of intelligent systems.
Emulation cannot learn if the observer does not, and the observer must be able to learn. This is called an observer loophole. This is the simplest explanation for the so-called social intelligence problem. In practice, the observer loophole makes the emulations look like real intelligent agents. But they have all their inherent limitations.
Emulation also fails if the emulated system has problems that the observer is not aware of. If the observer cannot tell that the emulated system has problems, he cannot learn from these problems.
This brings us to the final problem with emulation: learning by emulation is only one mechanism by which intelligent systems can evolve. A true adaptive agent is intelligent because it is designed to evolve with the characteristics of an evolving system.
Emulation is useful for teaching how to build intelligent systems similar to intelligent systems. However, he cannot learn what an intelligent system can and cannot do, unlike an intelligent agent. This brings us to a very important question: is emulation really a method of studying complex intelligent systems?
And for the design philosophy to work, it was important to make sure that we can not only learn about responsive emulators, but also improve them. Therefore, we carefully studied adaptive emulators and developed a system that could learn from them. This learning process began with the ability to define adaptive emulators. Then, by chance, a slightly better method of identifying them came along, which allowed us to create a responsive emulator with very high usability.
Cybernetics has evolved in ways that distinguish first-order cybernetics (about observable systems) from second-order cybernetics (about observing systems) – in particular, second-order cybernetics is usually associated with systems that control and act on each other – and differs from modern cybernetics mainly when it comes to questions about whether reflexivity or reflection has an explanatory role.
In cybernetics, the phenomena of time and space are identical to physical phenomena in that there are fundamental problems of theory and measurement.
The first order cybernetics concept is the observer concept. Second order cybernetics is the theoretical practice of cybernetics. Second-order cybernetics makes no distinction between cybernetics and modeling. This approach was a fundamental principle of cybernetics as applied to physical systems. It also suggests that cybernetics is not a model, but a tool for understanding phenomena and systems.
Cybernetic intelligence can use a logical-linguistic system to transform communication data into machine instructions. Such a system can use the well-known Fisher-Simon transform to convert instructions to data. This allows the system to directly translate from syntactic forms, which in turn allows the system to understand the language in a statistical sense. This idea theoretically suggests that a cybernetic system can act on a third party (for example, a person), and he can act as an intermediary between a person and a computer, or vice versa.
A cybernetic automaton is a hypothetical (albeit mathematically possible) system that simulates a physical system (for example, a machine).
The design of self-regulating control systems for a planned economy in real time was studied in the 1950s. A good example is the rational programmer method, which claims that the rational planning method can be used to design control systems. This method, although somewhat abstracted, can be understood in terms of feedback control theory. The main idea behind the rational programmer method was that real-time planned economies like those developed in the Soviet Union could be planned using the rational programmer method. A rational planner manages a system of rational rules by thinking in terms of programs and control systems.
In a rational control system, the planner does not need to be aware of all the activities that the system is performing. Instead, the planner must make decisions based on observable data and improve the system, for example by creating more «rational» rules and more «efficient» data processing mechanisms. Many «pre-programmed» control systems use feedback to automatically improve the system over time. Examples include most industrial automation and industrial robots, as well as many process control systems.
Process simulation
Ethan Zhang has developed a number of approaches to creating self-modifying systems. Among other things, Zhang described the problem of designing and implementing reliable controls.
Here is an example of a self-modifying control system. One way to think of such a system is that it is like a closed-loop controller, where there is some influence of the system on the controller, and the controller is to some extent the input or output of the system.
Process modeling is the process of transforming data from a physical environment into a graphical representation and allowing the user to interact with that environment. Complex technological systems require efficient and powerful modeling. This includes adaptive modeling and assessment techniques, predictive analytics, and analysis of end-to-end system behavior such as total life cycle cost, economic impact, and potential for failure. Digital process control encompasses many subsectors. They differ according to the specific tasks of the subject area. Although it provides control over the operation of machines, digital process control provides the ability to obtain in-depth information about the behavior of machines and the information flow between them. Process simulation allows operators to remotely monitor, troubleshoot, and automate process activities. It combines simulation, simulation and control to optimize and control the overall performance of a process, equipment or production line.
Controlling the output devices of an automated system often requires some knowledge of the system; this knowledge can be simulated on a computer, and these simulation tools can be used to remotely simulate equipment performance, system automation, and manufactured parts quality. These simulators are also a common way to access internal system information.
Cybernetics studies control systems as a concept, trying to discover the basic principles underlying things like behavior, motivation, learning, and goal pursuit.
Although this is the broadest sense of cybernetics, the exact scope of cybernetics in this broader sense is not entirely clear, since cybernetics in a narrower applied sense is often limited to the «higher» problems of managing communication systems. In applications of cybernetics to the human system, this understanding of cybernetics usually differs. Cybernetics is a view of complexity. That is, cybernetics studies how things connect and the interactions that occur between them. Cybernetics has received widespread attention thanks to research in computer science, systems theory, and information theory. Much of this research, especially in fields such as robotics, incorporates ideas from cybernetics and looks at the human systems that underlie them.
Cybernetics is the study of how complex feedback systems, feedback loops, and other dynamic processes interact to create a complex organization. It covers management theory and social and economic systems. In its simplest form, cybernetics studies feedback systems, which are usually «motivated by one or more goals, which may be known or unknown». It is often studied alongside other fields such as mechanics, electronics, mechanical engineering, manufacturing, and others such as economics.
Control theory and feedback loops in one form or another can be found in most modern technologies. For example, in systems engineering, control theory is used to design and implement automated control systems, and in operations research, control theory is used in linear programming problems. In computer science and systems engineering, feedback loops are also used to design and implement electronic communication networks. Many modern forms of computing have both a control theory-based implementation and a model-based or cybernetics-based abstraction.
In architectural theory, cybernetics has a long and sometimes controversial history. The philosophy of cybernetics has a built-in concept according to which the goal of the scientific study of the phenomena of life is to achieve intellectual control over them. In other words, cybernetics sought to achieve absolute control over the subject. Since cybernetics deals with the mechanics of a complex system, it has a profound effect on the physical aspects of building construction. An example of this is the construction of the Olympic Village for the 1996 Summer Olympics, which was recognized by the American Society of Civil Engineers as the most advanced technology in architecture because it used embedded data-driven computer systems to control the buildings. Computer systems provided efficient waste disposal to save money on sanitation. The main buildings have been optimized for energy efficiency and designed so that simple power cables can be easily replaced. This resulted in less damage to buildings in the event of a fire. The buildings were built with many devices and computer control. In many ways, the Olympic Village is a symbol of the utopian cybernetic architectural movement.
Biology
Theories of human behavior and decision making have been known for hundreds of years. However, it is only relatively recently that psychologists have gained an understanding of the factors that contribute to human decision-making and how their decisions are influenced by sensory, motor and cognitive information processing. Modern psychology studies the influence of these factors on people in order to understand how people think, act and interact with each other. Modern discoveries in psychology are using computers to aid in their experiments. Computers can simulate processes in the human brain and allow researchers to conduct new experiments in this area.
The revolution in molecular genetics has become a watershed for neuroscientists, as molecular biology, coupled with relatively recent advances in electrochemistry and optogenetics, has created many experimental tools for studying the brain. This allowed neuroscientists to understand the functional architecture and organization of the brain, and to determine the role of neural networks in the brain, especially in cognition. The idea of neural networks in the brain arose from the study of a biological model of the nervous system, in which cells were divided into specific functionally integrated groups, with groups with the same type of function having the strongest relationship. Thanks to the discovery of the molecular genetic foundations of the functioning of the nervous system, such as the role of transcription factors and proteins in the formation of the neural network, as well as the biophysics of gene expression, a number of genetic tools have emerged to study the relationship between the molecular genetics of neural networks. It is now understood that the neurobiological mechanisms underlying cognition are the result of global brain networks formed by tens of thousands of neuronal cells.
Developments in neuroscience include brain imaging, bioelectrical impedance analysis, magnetic resonance imaging, functional magnetic resonance imaging, transcranial magnetic stimulation, and electroencephalography, which are some of the most important tools for neurobiological research.
These tools also play an important role in connecting the brain’s neural networks with the cerebral cortex, which is responsible for our higher cognitive functions such as language, perception, memory, attention, thinking, reasoning, and emotion. It’s a link that hasn’t been made before, linking the functional architecture of an area of the brain to the fundamental workings of that area.
Neuropsychology is a branch of psychology devoted to the study of the psychology of human consciousness and the human mind in general. Neuropsychology tries to understand the human mind on a neural basis, that is, through the interaction of various neural circuits (neurons and synapses). It is also considered to be the most rigorous and reliable branch of clinical psychology. It can help people with all aspects of the functioning of the brain, mind and body and can help provide answers to the questions: «What happens in the brain during mental processes»? and «What are the neural foundations of such processes as: sight, hearing, emotion, memory, self-awareness, thought, decision-making, thought process, language, understanding, reasoning, sleep or consciousness»? It provides a common language and common language that transcends different disciplines. Neuropsychology tries to be a comprehensive science in the field of psychology and science, understanding the mechanisms underlying human mental functions and using the results of the cognitive, neurological and physiological sciences to elucidate the basic processes and explain human cognition. Neuropsychology studies the emergence of the human mind from its neural sources and is the leading scientific field of research on the brain and behavior.
Research into the neurochemistry of motivation and skill acquisition in sports has led to a better understanding of the interaction between skill acquisition and brain physiology. Musicians’ research has shown that although they have a large frontal cortex, most of the emotional areas of their brains are overwhelmed by incoming information. This makes it necessary to constantly switch attention from the concrete details of the problem to abstract thinking. The same effect can be seen with high-level artists; they must constantly shift their attention from analyzing a work of art to working on another.
Research has shown that the hippocampus is involved in decision making. The hippocampus participates in memory and decision making by providing input during decision processing. The left-lateralized hippocampus is involved in decision-making that is more efficiently processed by the influence of the left hemisphere. This activation of the right hemisphere of the brain when making a decision is known as the lateralized effect. In other words, the right hemisphere is more likely to use logic and reasoning when making decisions, while the left hemisphere uses emotions and feelings to make decisions. The right hemisphere uses affect as its base input, while the left hemisphere uses analytical reasoning. The right hemisphere of the brain is more dominant in decision making. Studies of lateralized decision making in both the right and left hemispheres of the brain have shown that processing in the left hemisphere during decision making aids coding of information in episodic memory and decision making. The activation of the right hemisphere during decision making is associated with gamma fluctuations in the cerebral cortex. It was found that gamma waves in the cerebral cortex are triggered by encoding incoming sensory data into episodic memory. The hippocampus is involved in the restoration and maintenance of long-term memory. The hippocampus works in conjunction with the medial temporal lobe and entorhinal cortex to create spatial memory. Consequently, the processing of spatial information leads to the creation of episodic memory. It has also been observed that the right hemisphere of the brain can access episodic memory more easily. This may be due to the fact that the right side of the brain plays a much more important role in spatial processing and generation of information in long-term memory. This confirms the hypothesis that the right side of the brain is better at extracting and processing spatial information.
The medial temporal lobe is associated with short-term memory, semantic memory, language, speech production, language understanding and production, speech processing, facial processing, and emotional and prosodic processing. The hippocampus is closely associated with the medial temporal lobe, including Broca’s area and Wernicke’s area.
It has been suggested that the medial temporal lobe is associated with memory consolidation, whereas the hippocampus may be responsible for retention, learning, and knowledge. This is supported by studies that show that during consolidation, hippocampal formation works to integrate episodic memory and choose which information to retain from the original memory. In contrast, Wernicke’s zone and associative zone help maintain the integrity and availability of memory.
Many other brain structures contribute to memory. However, the hippocampus is primarily responsible for creating space-time maps. The hippocampus plays an important role in contextual information. For example, space-time maps are used to figure out where a body is in relation to its environment. The medial temporal lobe is associated with complex thinking, speech, and higher cognitive functions. In addition, it plays an important role in the processing of emotions. The medial temporal lobe also functions as a connection between the two hemispheres of the brain and aids in emotional and affective processing.
