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SEMINAR REPORT ON YBERNETICS
As first defined by Norbert Wiener Cybernetic is the Control and communication in the animal and the machine. The principles of cybernetics have been applied in many fields. The errands of cybernetics application are including the extending of application domain, subdividing the problem, and building of reliability features, dealing of parallel and concurrent computation, handling of error states, and creating of precision requirements. This paper gives the small overview of cybernetics. It also gives brief information about software cybernetics. In particular, we try to formulate the goal-oriented requirements analysis process as a feedback control system, in which a classical divide and conquer design philosophy is turned into a continuous augmentation process to existing design towards an optimal one.
Software cybernetics is an emerging area that explores the interplay between software and control. The controlled Markov chain (CMC) approach to software testing supports the idea of software cybernetics by treating software testing as a control problem, where the software under test serves as a controlled object modeled by a controlled Markov chain and the software testing strategy serves as the corresponding controller. The software under test and the corresponding software testing strategy form a closed-loop feedback control system. The theory of controlled Markov chains is used to design and optimize the testing strategy in accordance with the testing/reliability goal given explicitly and a priori.
Self adaptive software is becoming more and more important and topical. As a new emerging discipline, self-adaptive software has strong background in control theory. This paper also analyses the similarity between software self adaptation technologies and control theory, and shows that self-adaptive software could be studied from software cybernetic perspective.
Many research areas have already benefited from the use of the concepts from software cybernetics although their relationship with software cybernetics has not yet been explicitly determined. Some of these contributions are described next.
The term cybernetics stems from the Greek (steersman, governor, pilot, or rudder - the same root as government). Cybernetics is a broad field of study, but the essential goal of cybernetics is to understand and define the functions and processes of systems that have goals, and that participate in circular, causal chains that move from action to sensing to comparison with desired goal, and again to action. Studies in cybernetics provide a means for examining the design and function of any system, including social systems such as business management and organizational learning, including for the purpose of making them more efficient and effective.
Chapter 1 gives the introduction of the concept. It tells us why the concept has come into existence & the importance of the concept. It also gives the history behind the concept. It also tries to define the term.
Chapter 2 deals with the various areas related to the topic of cybernetics.
Chapter 3 gives the principals of the cybernetic. It also gives one example of simple cybernetic model.
Chapter 4 tells about the core principle of cybernetic i.e. Feedback. This chapter tells the importance of feedback or how the feedback is used in the various systems.
As the concept is very confusing, many of the people think that itâ„¢s like AI only. So Chapter 5 gives the relation of cybernetic and Artificial Intelligence.
Chapter 6 consider the area of cybernetics i.e. Software Cybernetics. It covers the details of software cybernetics. Concept Control Markov Chain approach for the Software Testing.
Chapter 7 explores the concept of Self Adaptive Software in prospect with the cybernetics, which is emerging area in the cybernetics.
Chapter 8 gives various research topics of Software Cybernetics. Various interesting research topics have covered in this chapter.
Chapter 9 tells the future of the area cybernetics.
2. Why Cybernetics
In the 1940s a group of academics from different disciplines held a series of meetings, where they discussed their interests. It became apparent that, despite their different subjects, they were interested in similar themes - particularly the use of control and communication - in different systems.
To progress their work further they were 'hampered by the lack of unity of the literature' for these themes and by the lack of a 'common terminology' - so they felt the need to have a new subject for this work. On the basis that existing terminology was too biased towards one of the existing subjects, they decide there was a need for a new name for the subject - and they decided to call the field of 'control and communication theory', whether in the machine or in the animal, by the name Cybernetics, derived from the Greek Kubernetes or steersman.
As further justification for the name, the equivalent Latin word gives us the word governor, meaning a person in control. An early automatic control system was a speed governor for a steam engine. Also, Ampere used the work cybernetique in 1834 for his science of government (people who think they are in control).
Norbert Wiener, an applied mathematician, published a book in 1948, called Cybernetics - or control and communication in the animal and the machine. Other people involved at the time included Arturo Rosenblueth, a physiologist; Warren McCulloch, who with Walter Pitts produced the first model of a neuron (the basic processing element of brains), Margaret Mead, Frank Fremont Smith the medical director of the Josiah Macy Foundation, and John Von Neumann, a pioneer of Computer Science.
3. Cybernetics and the steersman
FIG 1.3.1Cybernetics & Steersman
Feedback can be very useful - particularly for control. Consider the case of the steersman (FIG 1.3.1) (which gives the name Cybernetics) responsible for ensuring a boat follows a given course despite the effects of winds and tides.
Without feedback, the steersman would perhaps point the boat towards the correct destination and in effect keep his eyes closed and hope that the boat ends up at the right place. However winds or tides would push the boat off course and the steersman would not know - as he can't see. Inevitably the boat will not go where it should.
FIG 1.3.2 Steersman giving direction to boat
The solution is feedback (FIG 1.3.2). The course the boat is following is found and that information is fedback to the steersman (achieved by the steersman looking to see where he is going!). If the boat is off course the steersman takes the appropriate action, turning the rudder left or right, to get back on course.
Effectively you see what your system (in this case the boat) is doing, and if it is not doing the right thing (here the boat is off course), suitable corrective action is taken (the rudder is turned left or right). It's a nice concept, easily extended to other systems.
4. Cybernetic Models
Cybernetic models are applied prescriptively in design and descriptively as explanatory devices. Their prescriptive use savours of engineering. They are employed in the specification of controllers and regulators for industrial plants, navigation, and so on. The most interesting developments have occurred in the area of predictive, adaptive, and optimizing controllers, usually able to deal with randomly perturbed environments. On the other hand, cybernetic models are widely used in determining the proper relationship between a man and a machine, for example, in the design of vehicle-control systems. Another field of application is teaching and training. Here, training is literally interpreted as the control of a human learning process and insofar as an adequate model exists, the training instructor may be partially or wholly replaced by a suitable machine. In operational research, cybernetic models are used to specify stockholding schemes, process and assembly programming, and inventory control. They are also used in a normative fashion; for example the management of a business enterprise is often modeled as a game-like decision and control process.
Descriptive applications are legion. At a neuro physiological level, cybernetic models have been used to explain many aspects of the working of a brain. Five areas are of special importance: models for simplified neural networks, chiefly representing perceptual processes; statistical models for the complex oscillations and regulations of real neural activity; models relating algorithms or plans (cited earlier) to the conditioning process; models for the mechanisms responsible for maintaining and directing attention; and models for the detailed changes that occur at the synaptic junctions between neurons.
Outside the brain, cybernetic principles are widely used to elucidate the control of bodily functions (autonomic processes, hormone-mediated regulatory systems, muscular control, and so on).A surprisingly large amount of molecular biology and bio-chemistry also rests upon models depicting the organization of enzyme systems and the hierarchical control of enzyme synthesis. This type of explanation promises to have further utility in relating genetically coded instructions to the cellular economy. Cybernetic models have been used in embryology since the early 1950s, and some of the original schemes have now been formulated in a detailed mathematical fashion.
Within psychology, it is possible to explain several classes of behaviour and cognition in terms of hierarchies of control systems. The previously stated notions of planning and learning are pertinent to this field. At a macroscopic level, cybernetic ideas are applied to interpersonal interactions such as conversations, the communicative behaviour of small groups, and the homeostatic processes maintaining the status quo in social systems. Indeed, one of the first essays in this direction took place in the context of social anthropology where cybernetic ideas are becoming of greater importance. Somewhat similar developments have occurred in the animal domain; ethnologists use cybernetics freely, especially in dealing with population density control systems and the regulation of reproduction.
In the 1940s a group of academics from different disciplines held a series of meetings, later called the Macy Conferences, where they discussed their interests. It became apparent that, despite their different subjects, they were interested in similar themes - particularly the use of control and communication - in different systems.
To progress their work further they were 'hampered by the lack of unity of the literature' for these themes and by the lack of a 'common terminology' - so they felt the need to have a new subject for this work. On the basis that existing terminology was too biased towards one of the existing subjects, they decide there was a need for a new name for the subject - and they decided to call the field of 'control and communication theory', whether in the machine or in the animal, by the name Cybernetics, derived from the Greek Kubernetes or Ëœsteersmanâ„¢.
As further justification for the name, the equivalent Latin word gives us the word governor, meaning a person in control. An early automatic control system was a speed governor for a steam engine. Also, Ampere used the work cybernetique in 1834 for his science of government (people who think they are in control).
Macy conferences were then devoted to, new cybernetics, and opened with two presentations: the first by von Neumann on the new computing machines, followed by neurobiologist Lorente de No on the electric properties of the nervous system. These circuiting of analogies between behaviour of computers and the nervous system became central to cybernetic imagination and its founding desire to define the essential "unity of a set of problems" organized around "communication, control, and statistical mechanics, whether in the machine or living tissue. In particular, the early cyberneticists are convinced that research on computers and the organization of the human brain are one and the same field, that is, "the subject embracing both the engineering and the neurology aspect is essentially one."
Wiener defined cybernetics in 1948 as the study of "control and communication of the animal and the machine". This definition captures the original ambition of cybernetics to appear as a unified theory of behaviour of living organisms and machines, viewed as systems governed by the same physical laws. The initial phase of cybernetics involved disciplines more or less directly related to the study of those systems, like communication and control engineering, biology, psychology, logic, and neurophysiology. Very soon, a number of attempts were made to place the concept of control at the focus of analysis also in other fields, such as economics, sociology, and anthropology. The ambition of "classic" cybernetics thus seemed to involve also several human sciences, as it developed in a highly interdisciplinary approach, aimed at seeking common concepts and methods in rather different disciplines. In classic cybernetics this ambition did not produce the desired results and new approaches had to be attempted in order to achieve them, at least partially.
In the 1970s New cybernetics has emerged in multiple fields, first in biology. Some biologists influenced by cybernetic concepts (Maturana and Varela, 1980); Varela, 1979; Atlan, 1979) realized that the cybernetic metaphors of the program upon which molecular biology had been based rendered a conception of the autonomy of the living being impossible. Consequently, these thinkers were led to invent a new cybernetics, one more suited to the organization of mankind discovers in nature - organizations he has not himself invented. The possibility that this new cybernetics could also account for social forms of organization, remained an object of debate among theoreticians on self-organization in the 1980s.
In political science in the 1980s unlike its predecessor, the new cybernetics concerns itself with the interaction of autonomous political actors and subgroups and the practical can reflexive consciousness of the subject who produce and reproduce the structure of political community. A dominant consideration is that of recursiveness, or self-reference of political action both with regards to the expression of political consciousness and with the ways in which systems build upon themselves.
Geyer and van der Zouwen in 1978 discuss a number of characteristics of the merging "new cybernetics". One characteristic of new cybernetics is that it views information as construct and reconstructed by an individual interacting with the environment. This provides an epistemological foundation of science, by viewing it as observer-dependent. Another characteristic of the new cybernetics is its contribution towards bridging the "micro-macro gap". That is, it link the individual with the society. Geyer and van derZouten also noted that a transition form classical cybernetics to the new cybernetics involves a transition form classical problems to new problems. These shifts in the thinking involve, among others a change form emphasis on the system being steered to the system doing the steering, and the factor which guide the steering decisions. And new emphasis on communication between several systems which are trying to steer each other.
Recent endeavors into the true focus of cybernetics, systems of control and emergent behavior, by such related fields as Game Theory (the analysis of group interaction), systems of feedback in evolution, and Metamaterials (the study of materials with properties beyond the Newtonian properties of their constituent atoms), have led to a revived interest in this increasingly relevant field.
6. Defining the term Cybernetics
There are many definitions of cybernetics and many individuals who have influenced the direction of cybernetics. Cybernetics treats not things but ways of behaving. It does not ask "what is this thing" but "what does it do" and "what can it do" Because numerous systems in the living, social and technological world may be understood in this way, cybernetics cuts across many traditional disciplinary boundaries. The concepts which cyberneticians develop thus form a metadisciplinary language through which we may better understand and modify our world.
Cybernetics seeks to develop general theories of communication within complex systems. ... The abstract and often formal mathematical nature of its aim ... makes cybernetics applicable to any empirical domain in which processes of communication and their numerous correlates occur. Applications of cybernetics are widespread, notably In the computer and information sciences, in the natural and social sciences, in politics, education and management."
"Cybernetic= the art of governing or the science of government"
- A.M. Ampere
"The art of steersman ship"
- W. Ross Ashby
"A branch of mathematics dealing with problems of control, recursiveness, and information"
- Gregory Bateson
Cybernetics is concerned with scientific investigation of systemic processes of a highly varied nature, including such phenomena as regulation, information processing, information storage, adaptation, self-organization, self-reproduction, and strategic behavior. Within the general cybernetic approach, the following theoretical fields have developed: systems theory (system), communication theory, game theory, and decision theory."
"So a great variety of systems in technology and in living nature follow the feedback scheme, and it is well-known that a new discipline, called Cybernetics, was introduced by Norbert Wiener to deal with these phenomena. The theory tries to show that mechanisms of a feedback nature are the base of teleological or purposeful behavior in man-made machines as well as in living organisms, and in social systems."
- Ludwig von Bertalanffy
"The science of effective organization"
- Stafford Beer
"is also sometimes used as an umbrella term for a great variety of related disciplines: general systems theory, information theory, system dynamics, dynamic systems theory, including catastrophe theory, chaos theory, etc."
- Bruce Buchanan
"the art of securing efficient operation"
- L. Couffignal
"The single most important property of a cybernetic system is that it is controlled by the relationship between endogenous goals and the external environment."
- Peter Corning
"Cybernetics is a science of purposeful behavior. It helps us explain behavior as the continuous action of someone (or thing) inthe process, as we see it, of maintaining certain conditions near a goal state, or purpose."
- Jeff Dooley
"Cybernetics is the science of unseen processes which energize dynamic entities: man-made, natural, and spiritual. In a narrower technical view, cybernetics are what makes systems function."
- Charles A. Fink
"Cybernetics studies organization, communication and control in complex systems by focusing on circular (feedback) mechanisms Cybernetics, deriving from the Greek word for steersman (kybernetes), was first introduced by the mathematician Wiener, as the science of communication and control in the animal and the machine (to which we now might add: in society and in individual human beings). It grew out of Shannon's information theory, which was designed to optimize the transmission of information through communication channels, and the feedback concept used in engineering control systems. In its present incarnation of "second-order cybernetics", its emphasis is on how observers construct models of the systems with which they interact."
"Cybernetics could be thought of as a recently developed science, although to some extent it cuts across existing sciences. If we think of Physics, Chemistry, Biology, etc. as traditional sciences, then Cybernetics is a classification which cuts across them all. ...Cybernetics is formally defined as the science of control and communication in animals, men and machines. It extracts, from whatever context, that which is concerned with information processing and control. ... One major characteristic of Cybernetics is its preoccupation with the construction of models and here it overlaps operational research. Cybernetic models are usually distinguished by being hierarchical, adaptive and making permanent use of feedback loops. ... Cybernetics in some ways is like the science of organisation, with special emphasis on the dynamic nature of the system being organised."
- F. H. George
"Cybernetics is essentially about circularity."
- Ranulph Glanville
"Cybernetics, as we all know, can be described in many ways. My cybernetics is neither mathematical nor formalized. The way I would describe it today is this: Cybernetics is the art of creating equilibrium in a world of possibilities and constraints."
- Ernst von Glasersfeld
"The theory of interconnectedness of possible dynamic self-regulated systems with their subsystems"
- G. Klaus
"Cybernetics is the science of effective organization, of control and communication in animals and machines. It is the art of steersmanship, of regulation and stability. The concern here is with function, not construction, in providing regular and reproducible behaviour in the presence of disturbances. Here the emphasis is on families of solutions, ways of arranging matters that can apply to all forms of systems, whatever the material or design employed. ... This science concerns the effects of inputs on outputs, but in the sense that the output state is desired to be constant or predictable - we wish the system to maintain an equilibrium state. It is applicable mostly to complex systems and to coupled systems, and uses the concepts of feedback and transformations (mappings from input to output) to effect the desired invariance or stability in the result."
- Chris Lucas
"Originally the study of biological and artificial control systems, cybernetics has evolved into many disparate areas of study, with research in many disciplines, including computer science, social philosophy and epistemology. In general, cybernetics is concerned with discovering what
mechanisms control systems, and in particular, how systems regulate themselves."
"Cybernetics is simultaneously the most important science of the age and the least recognized and understood. It is neither robotics nor freezing dead people. It is not limited to computer applications and it has as much to say about human interactions as it does about machine intelligence. Today's cybernetics is at the root of major revolutions in biology, artificial intelligence, neural modeling, psychology, education, and mathematics. At last there is a unifying framework that suspends long-held differences between science and art, and between external reality and internal belief."
- Paul Pangaro
"Cybernetics is the study of systems which can be mapped using loops (or more complicated looping structures) in the network defining the flow of information. Systems of automatic control will of necessity use at least one loop of information flow providing feedback."
- Alan Scrivener
"Cybernetics is the study of man in relation to his particular job or machine with special reference to mental processes and control mechanisms."
- Times of London
- May 11, 1959
Cybernetics was an experimental epistemology concerned with the communication within an observer and between the observer and his environment.
- Warren McCulloch
ËœËœControl and communication in the animal and the machine
- Norbert Wiener
"Cybernetics is a coverall word to describe the study of systems - of robots, computers, machines, and the people who use them."
- University of Bradford
"The name was coined by Norbert Wiener in 1948 as a result of collaborations between Wiener, a mathematician, and colleagues from other disciplines: they noticed that they had similar interests, but where was no name to group together their interests. They chose cybernetics, subtitle control and communication in the animal and the machine, thus reflecting that both technological and biological systems have many common characteristics."
- University of Reading
"Cybernetics is the study of systems which can be mapped using loops (or more complicated looping structures) in the network defining the flow of information. Systems of automatic control will of necessity use at least one loop of information flow providing feedback."
- Alan Scrivener
"a science concerned with the study of systems of any nature which are capable of receiving, storing, and processing information so as to use it for control"
- A.N. Kolmogorov
Chapter 2: Various Areas of Cybernetics
Cybernetics is an earlier but still-used generic term for many subject matters. These subjects also extend into many others areas of science, but are united in their study of control of systems.
The tendency to adopt a restricted interpretation for the term has been largely reversed but, as a result of its existence for a number of years, much of the American literature on cybernetics (in the general sense) appears in connection with the disciplines to which it was applied, or under specialized headings. Among the most important of these are: General Systems Theory, Bionics (q.v.; Biological Cybernetics, using the organizational principles of living systems in the design of artifacts), the theory of "Self Organizing" or evolutionary systems and "Artificial Intelligence." The last area includes the study of computer programs that are guaranteed to solve problems (namely algorithms) and "heuristic" programs that make "intelligent" (and often very economic) shortcuts in problem solving but that are not necessarily guaranteed to work on all occasions.
1. Pure Cybernetics
Pure cybernetics has an axiomatic and a philosophical aspect. The axiomatic paradigm is to assume certain postulates about a system and to deduce the system properties (such as reproduction, differentiation, learning) that are consequences of these assumptions. The philosophical branch of the science is often concerned with theories; for example, the theory of simplification (how the complex properties of a real system can be reduced to manageable proportions without losing essential information) and the theory of commands. But it is also concerned with the issue of relevance as well as with the proper identification between different types of cybernetic model and real assemblies.
Pure cybernetics studies systems of control as a concept, attempting to discover the basic principles underlying such things as
FIG 2.1.1 ASIMO navigating stairs
ASIMO uses sensors and intelligent algorithms to avoid obstacles and navigate stairs. See FIG 2.1.1.
2. Medical cybernetics
Medical cybernetics investigates networks in human biology, medical decision making and the information processing structures in the living organism.
Cybernetics in biology is the study of cybernetic systems present in biological organisms, primarily focusing on how animals adapt to their environment, and how information in the form of genes is passed from generation to generation. There is also a secondary focus on cyborgs. Biological Cybernetics investigates communication and control processes in living organisms and ecosystems. Bio-robotics is a term that loosely covers the fields of cybernetics, bionics and even genetic engineering as a collective study.
4. Complexity Science
Complexity Science attempts to analyze the nature of complex systems, and the reasons behind their unusual properties.
FIG 2.4.1 Complex Adaptive System
Cybernetics becomes a way of modeling Complex Adaptive System. See FIG 2.4.1.
5. Computer Science
Computer science directly applies the concepts of cybernetics to the control of devices and the analysis of information. The development of the computer and its intrinsic discipline of mathematical logic has greatly increased the use of cybernetics during the past fifty years because it was now possible to process large amounts of data, better known as information processing.
6. Software Cybernetics
Software cybernetics is an emerging area that explores the interplay between software and control. Software cybernetics treat software testing as a control problem, where the software under test serves as a controlled object and the software testing strategy serves as the corresponding controller.
Cybernetics in engineering is used to analyze cascading failures and System Accidents, in which the small errors and imperfections in a system can generate disasters. Engineering cybernetics (or Technical cybernetics) deals with the question of control engineering of mechatronic systems. It is used to control or regulate such a system; more often the term control theory encompasses this field and is used instead. See the FIG 2.7.1 of Artificial Heart, which is the example of biomedical engineering object.
FIG 2.7.1An artificial heart
8. Organizational Cybernetics
Organizational Cybernetics (OC) studies organizational design, and the regulation and self-regulation of organizations from a systems theory perspective that also takes the social dimension into consideration. Researchers in economics, public administration and political science focus on the changes in institutions, organisation and mechanisms of social steering at various levels (sub-national, national, European, international) and in different sectors (including the private, semi-private and public sectors; the latter sector is emphasised).
Mathematical Cybernetics focuses on the factors of information, interaction of parts in systems, and the structure of systems.
The word cybernetics comes from a Greek term that means 'a helmsman who steers his ship to port.' Psycho-Cybernetics is a term I coined which means, "Steering your mind to a productive, useful goal .... so you can reach the greatest port in the world ... peace of mind. With it, you're somebody. Without it, you're nothing."
- Dr. Maxwell Maltz, author of 30 million copy best-seller Psycho-Cybernetics
By examining group behavior through the lens of cybernetics, sociology seeks the reasons for such spontaneous events as smart mobs and riots, as well as how communities develop rules, such as etiquette, by consensus without formal discussion. Affect Control Theory explains role behavior, emotions, and labeling theory in terms of homeostatic maintenance of sentiments associated with cultural categories. These and other cybernetic models in sociology are reviewed in a book edited by McClelland and Fararo.
12. Neuro Cybernetics
Neuro cybernetics is a science that covers the integration of machines, and much to the organism of a living being. The intercommunication between the nervous system and artificial appliances is a field on the verge of major breakthroughs...active research is still very much ongoing to make neuro-cybernetics/bio-cybernetics a comprehensive and fundamental science. Cyborg is the cybernetic object.
Chapter 3: Principles of Cybernetics
1. Principles of Cybernetics
In the next are introduced some generally known principles (laws) of cybernetics: the principle of uncertainty, the principle of feedback, the principle of exterior completion, the principle of requisite variety, the principle of reduction, the principle of optimality, the principle of homeostasis.
1.1 The principle of homeostasis
It expresses the fact that the base goal of control is homeostasis. This principle represents (with a certain analogy) the laws of conservation. Like in physics, these laws are tightly connected with the optimality principles (the variation principles). The homeostasis is the ability of system to preserve ultra stability, i.e. the conservation of stability at the changing external conditions.
The motion of systems is secured by programmed part of control, and the perturbation from nominal state is compensated by mechanism of feedback.
The problems of large-scale systems are not possible to realize only on the base of feedback. In this connection is necessary to stress, that in cybernetics is known, that the ideal control is open-loop control, which but isnâ„¢t possible wholly realize because of impossibility to create ideal model of controlled system. That is way every cybernetics system is from the viewpoint of control combined - there are exist also programmed part, and feedback only corrects the deviations from the desired state.
The complex system stability is substantially dependent on the stability of subsystems. But there is only the necessary, not sufficient condition. By influence of interactions can be the global system consisting of stable subsystems unstable.
But there is another type of stability mechanism in complex system. There are so-called discrete events which are arising on the background of natural system dynamics. By influence of these events arise not only slow changes but the abrupt structural ones.
There are discontinuous, discrete, far from equilibrium, and emergent behaviours (1982). The system stability depends on bifurcation of seemingly constant (control) parameters. The bifurcations of parameters is manifesting in the abrupt, uncontrolled chaotic, to a certain degree catastrophic situation. The number and character of these events is given by tremendous combinatorial complexity which is given by number of system elements (subsystems), as also their rich qualitative heterogeneity, which is generated by individual particularity of subsystems.
1.2. The principle of external completion
This principle has been formulated by S. Beer (1960). In the accordance with GÃƒÂ¶del theorem of incompletenesÃ‚Â¬s there is no language of control completely adequate to its mission. The introduced principle can be considered as a methodological base of hierarchical control system. The using of this principle we can find in the optimal control theory, where the criterion of optimality is the external completion of control problem. This drawback is possible to removed if we put the black box into control systems; black box is suitable model of cybernetic system if contains such an amount of information to overcome its variety (the measure of its complexity). The black box is referring to the decision procedures (these are expressed in the language of higher level which canâ„¢t be formulated in the language of basic level), and in this way compensates its lacks.
1.3. The principle of requisite variety
This principle has been formulated by W. R. Ashby (1956). The principle is connected with Shannonâ„¢s information theory, and expresses the fact, that the regulator power canâ„¢t be bigger than the capacity of the transmitting channel. One of the most important results of this principle is the assertion, that it is impossible to create the simple control system for the effective control of the complex system. The control system (regulator) must be as complex as the complex system to be controlled.
The important conclusion of this principle can be formulated in the so-called principle of adequateness of object and its model (e.g. invariant control systems and adaptive systems with reference model).
1.4. The principle of feedback
The feedback is one of the basic notions of cybernetics. From the philosophical point of view the feedback is the concretisation of the most important type of casual connection, the mutual activity, where the every process behaves as the cause and as the conclusion too.
1.5. The principle of intentionality:
The notion of intentionality is usually understood as a immanent characteristic of subject. The aim (goal) is created as a sure ideal picture to be attained. The expression of this principle is the phenomenon of optimality. The cybernetics is often characterized as a science of optimal control of the complicated systems. The problem of the optimality but is not wholly scrutinized from the philosophical and methodological point of view. The optimality is always connected with the chosen goal (criterion). From this viewpoint the all what has been occurred is optimal. It is a certain analogy of Hegel statement what is real is rational, and what is rational is real.
The indeed meaning the optimality obtain only with regard to the future events. The real control process is not a single act but the permanent activity which necessitate from the possibility to the reality. In this direction is very important the principle of freedom of option which includes the definite sequence of decision processes: in the given moment is necessary to control in such a manner in order to have possibility of option in the next time moment too.
The control processes are characterized by integral criterion-the attainment of control goal. The problematic of formulation of goals is the prerogative of a man. The classics of cybernetics consider in universality the homeostasis as the goal of control. But homeostasis is, so saying, the essence of the first order.
2. Goal Directed System
Cybernetics is primarily the science of constructing, manipulating, and applying cybernetic models which represent the organization of physical entities (such as animals, brains, societies, industrial plants, and machines) or symbolic entities (such as information systems, languages, and cognitive processes).
A paradigmatic organization, and the building block from which most cybernetic models are fabricated, is a "goal-directed system or "control system". Refer to the FIG 3.2.1. Such a system contains the following four - parts:
(1) Sensor (S): an abstractive process whereby the mediate state of the system's environment is described in terms of salient attributes or properties.
(2) Goal (G): the specification of a particular state of the system called the goal.
(3) Error Detection (E): a method for determining the deviation, if any, between the goal state and the intermediate state.
(4) Effectors (E'): a set of operations whereby the system can act upon and modify certain features of the environment which are relevant to or correlated with the descriptive properties.
These parts embody the following two rules:
(a)A rule asserting well-defined procedure for discovering, which of the possible actions are likely to bring the immediate state nearer to the goal state, and
(b)A rule whereby, given an instruction "Achieve Goal", the system acts upon its environment, guided by the deviation measure (or "difference signal") of the Error Detector, so that the goal state is approximated (the deviation is minimized).
Generally, the system replies to this instruction: by a statement "G is achieved", or a statement "So much effort has been expended in pursuit of G, but without success."
FIG 3.2.1 A Simple Goal Directed System
In the simplest cases, all parts of this specification are constant -with the possible exception of the instruction cited in (b). If the instruction is also constant, the goal-directed system is called a homeostat and continually seeks state (G). In general, however, some or all aspects of the specification are variable. Such an elementary device as a central heating controller forms to all of these requirements.
(1) Is identified - the thermometer of the thermostat that reads room temperature, (T).
(2) With the desired room temperature, T',
(3) Compares T- and T' and establishes which is the higher, and
(4) Is the furnace;
(a) Is the simple negative feedback rule: "turn on if room temperature, T, is less than T', otherwise turn off", and
(b) An instruction provided manually or by a timing apparatus.
But these conditions are also satisfied by very complex industrial and vehicle controllers and by natural systems at all levels of complexity. On a philosophical plane, the formalization of systems entailing the circular flow of information has resolved many of the dilemmas once engendered by teleology and purposiveness.
Nor is the goal-directed system necessarily tangible. A game (a business game or any simulation in the sense of game theory; q.v.) is also of this type. Further, a goal-directed system may be part of a computer program or a symbolic and problem-solving process. Analogy completion is typical in this respect.
Given the symbolic objects, A, B, and C, the system seeks the goal (of completing an analogy) by describing the relation of A to B and finding D (or modifying some existing D') so that the statement "A is to B as C is to D" is satisfied.
Cybernetic models are structures of mathematically related goal-directed systems, often combined with other elements such as logical operators and information storage media. The systems may be combined by coupling their variables (usually to yield a macro-system with properties in excess of those of its components). They can interact competitively (their goals remaining unchanged) or cooperatively, in which case, communication must take place in a suitable language to arrive at a compromise goal. A system with goal G1, may also be sequentially connected to a system with goal G2 in the sense that attainment of G1 delivers an instruction to achieve G2 (In this case, G1 and G2 are called sub goals of the goal of the conjoint system.) Finally, they can be organized into a hierarchy, in which only the lowest-level systems act upon the environment or have goals that refer to it directly. The higher-level systems sense lower-level properties and organize the lower-level systems.
Hierarchical structures comprehend such processes as planning and learning. In planning, a higher-level system Z is instructed to achieve an abstract goal, G. It forms a plan insofar as it can recognize that G entails the sub goals G1, and G2 which are in the repertoire of the available lower-level systems and insofar as it organizes them in sequence to attain G. Learning can be viewed as the system-organizing response of Z to a problem which remains insolvable until Z, has modified the characteristics of the lower-level systems in its domain.
Chapter 4: Feedback a Core of Cybernetic
Feedback is a useful principle, which can be applied to a great variety of systems, technological, involving animals and the environment. Feedback is a nice concept, easily extended to many systems. Let us see how feedback is useful in various systems.
1. Vehicle and robot control
FIG 4.1.1 A Vehicle System
Feedback has been shown to be useful for allowing a steersman to keep his boat on course - it can also be used, for instance, by a driver to keep a car on course. See the FIG 4.1.1. It can easily be adapted to ensure a car or any other vehicle is traveling at the right speed (particularly important when there is a speed camera!) despite hills or wind which can affect the speed.
Here the speed of the vehicle is measured (you look at the speedometer) and that information is fed back to the driver, who puts his foot on the accelerator to speed up, or eases his foot off the accelerator or even puts his foot on the brake if the car is going too fast.
You will note that the block diagram used to represent this system is very similar to that used for the steersman.
FIG 4.1.2 A Simple Robot System
The concept can also be applied to a manipulator robot, whose gripper is to be used, say, to pick up an object. Refer FIG 4.1.2. Here the current position of the gripper is measured and this is fed back to the robot's controller. Refer FIG 4.1.3. If the gripper is not in the right place, then the joints of the robot are moved, so that the gripper becomes in the right place. This too can be represented by a similar block diagram.
FIG 4.1.3 A Vehicle System
In fact it is slightly more complicated, in that for the gripper to be in the right place, each joint has to be at the right angle, an d that is achieved by having such a feedback control system for each joint. But the concept is the same.
2. Temperature control
We have seen feedback for use in controlling the course followed by a boat, for controlling the speed of a car, and for controlling the position of the end of a robot arm. In each case the systems are represented by almost identical diagrams. The concept can be extended easily for temperature control, both by machine and in humans and other animals - reflecting Wiener's definition - control and communication in the animal and the machine.
FIG 4.2.1 A Temperature Control System
First consider a system most have encountered in their house - a central heating system, but extended to have air conditioning as well so as to be able to heat and cool. Here the aim is to control the temperature of a room despite factors likely to change the temperature - such a drafts and windows, the sun coming though the window, people or PCs in the room heating it up, etc.
Again the output (room temperature) is measured, say by a thermostat, and that information is feedback to the boiler / air conditioning system, which then either heats or cools the room. Refer FIG 4.2.1
FIG 4.2.2 A Temperature Control System
The same concept applies to temperature control of the human body - despite being in a sauna or the Antarctic , if you are well your body temperature is at around 37 degrees - how is that maintained Again there are actions to heat or cool, as appropriate, and a similar diagram can be used.
Your body senses and feeds back the current temperature - and acts accordingly. If you are too hot, you sweat (or perspire) to cool. If you are too cold you shiver to warm up. If you are still too cold, you turn up the central heating.
As we have seen, feedback can be used to control systems, by measuring the output, feeding it back, and producing suitable corrective action if needed. It all sounds simple, but much effort is needed to achieve good control - we have a three year degree Cybernetics & Control Engineering concentrating on this.
If a system is complicated, particularly if (as often happens) it changes as it operates, a more sophisticated control mechanism is needed, one which adapts to changes in the system - the system must be able to learn.
FIG 4.3.1 A Learning Process
Learning is a feedback process. As shown in the FIG 4.3.1. As typified by the statement 'you learn by your mistakes'. You do something, assess how well it was done, and on that basis you refine and next time you do it differently (hopefully better). Again the usual block diagram can be adapted here.
As a test bed for research into how systems learn, we have used our simple mobile robots. See FIG 4.3.2. These can move around and perceive their environment through simple sensors. Students program the 'rules' to allow the robots to move around either avoiding obstacles, or follow objects - an interesting problem solving experiment.
FIG 4.3.2 A Vehicle System
Then we considered how such a device could learn those same rules. This is done by the robot using trial and error. They try an action, see if it was successful. If so, then that action is more likely to be used in that situation. If not the action is less likely to be used.
4. Neural networks
FIG 4.4.1 Brain comprises of Neurons
We have seen a need for systems to learn (a process which involves feedback) - but how can this be implemented Can we in fact produce systems that are 'intelligent' We could use a computer, suitably programmed - which in effect has one (or a few) 'processing' elements. However, even modern computers are not that advanced - perhaps it would be better to develop systems more like the most powerful learning systems - brains.
A brain comprises simple processing elements, called neurons. See FIG 4.4.1. Those act rather slowly. Typically brain does 1000 operations a second, whereas a computer does many millions. However, the brain has billions of neurons - connected together in a network, as shown in FIG 4.4.2. The net result of which is much more powerful than a normal computer.
FIG 4.4.2 Neurons-connected together in network
Thus we 'borrow' from nature and try to develop artificial neural networks ANNs - being many neurons connected together. There are many ways of implementing these, but one method is to have neurons which multiply each input by its 'weight' being a value associated with the connecting link to the neuron, and the neuron output is the sum of all such weights. The neuron output may well provide the input to other neurons.
FIG 4.4.3 Brain System
So that such a network can generate the 'right' results for any system, the correct weights are needed, but finding them is non trivial. So a 'training set' of inputs and correct answers is provided.
For each set, the inputs are passed to the network. The outputs are calculated, and any error between this calculated outputs and the expected outputs are used to adjust the weights. It is a feedback process, as shown in FIG 4.4.3.
5. Virtual Reality and Human Computer Interaction
Feedback we have seen for control and for learning. It is also used in the interaction between humans and machines, such as computers: human-computer interaction or HCI.
In fact, when you use a mouse to position the cursor you are using feedback - you look at the cursor on the screen and move the mouse until the cursor is correctly placed - or is it that the computer moves the cursor until you have put the mouse in the right place
FIG 4.5.1 A Virtual Reality System
This is very simple HCI - more sophisticated HCI is Virtual Reality. See FIG 4.5.1. Here a computer generates the necessary information so that the human can seem as if he or she is in an artificial world - i.e. the computer generates what the world looks like, perhaps sounds like, smells like and feels like (for which haptics is needed - a speciality at Reading).
But, were the human to turn his/her head, the world should look different - so the computer has to generate a new image of the world.
FIG 4.5.2 Human Computer Interaction
This means that the system is a feedback process - with information generated by the computer being communicated to the human, and information (e.g. position of head) about the human being communicated to the computer. See FIG 4.5.2.
Note, there are related topics such as tele-operation, where the information passed to the human is that of a real world. Also augmented reality where artificial information is added to real world information: e.g. head-up displays for pilots.
6. Cybernetics and the environment
We have seen that feedback applies in technological systems and systems involving animals - it also applies to the environment - a complex set of interacting systems. There are interactions between different species, for instance, predator - prey systems, but also so called mutualistic systems where two species help each other.
FIG 4.6.1Environmental System
In fact, the Earth itself comprises feedback loops. This is illustrated by the Gaia hypothesis, postulated by James Lovelock, a former Visiting Professor to Cybernetics at Reading. At its strongest, the Gaia hypothesis states that the Earth is a self regulating cybernetic system, with feedback loops aimed at controlling temperature, the amount of oxygen, salinity of the sea, etc. Refer FIG 4.6.2.
This it does as a result of feedback in which life and the planet work together to their mutual advantage, producing conditions suitable for both Earth and the life on it. This concept was at first dismissed by many biologists who believed that life adapted to its environment. To demonstrate how life and the planet could interact, Lovelock produced a simple model, Daisy world.
Daisy world is a grey planet orbiting a sun which is heating up (like our own). In the soil are seeds of daisies which grow between 7 and 37 degrees, but grow best at 22 degrees. Initially the planet is too cold, but once it becomes warm enough, daisies grow and keep the temperature constant for a long period at 22 degrees. This happens due to feedback. Refer FIG 4.6.2.
FIG 4.6.2 Daises grows on the planet as a result of change in the Environment.
If the temperature is below the optimum, more black daisies grow, absorbing heat and heating the surrounding area. If the temperature is above the optimum, white daisies thrive which reflect heat away, thereby cooling the surrounding area.
Once again, a feedback system, with two opposite control actions, either heating or cooling the planet.
In these pages we have seen that feedback is a useful principle, which can be applied to a great variety of systems, technological, involving animals and the environment. It can also be applied to economic systems, but at Reading we don't pretend we can control the economy!
Feedback is one example of such a principle - Cybernetics demonstrates that you can take a concept developed in one application, and then use it in others. This is a very important ability, and being able to do so is very useful, and makes Cyberneticists very employable.
Cyberneticists also tend to take a 'systems approach': meaning they not only appreciate the area in which they work, but they also know how their work fits in well with the rest of the system - again a very employable skill.
These pages, of course, can only give a brief feel for the subject. In fact, they concentrate on so called first order Cybernetics, involving basic feedback loops. There also exists second order Cybernetics, in which systems have an observer which monitor and also influence what is happening in the system ... there have also been suggestions of the need for third order Cybernetics.
Chapter 5: Artificial Intelligence and Cybernetics
The term "cybernetics" has been widely misunderstood, perhaps for two broad reasons. First, its identity and boundary are difficult to grasp. The nature of its concepts and the breadth of its applications, as described above, make it difficult for non-practitioners to form a clear concept of cybernetics. This holds even for professionals of all sorts, as cybernetics never became a popular discipline in its own right; rather, its concepts and viewpoints seeped into many other disciplines, from sociology and psychology to design methods and post-modern thought. Second, the advent of the prefix "cyb" or "cyber" as a referent to either robots ("cyborgs") or the Internet ("cyberspace") further diluted its meaning, to the point of serious confusion to everyone except the small number of cybernetic experts.
However, the concepts and origins of cybernetics have become of greater interest recently, especially since around the year 2000. Lack of success by AI to create intelligent machines has increased curiosity toward alternative views of what a brain does [Ashby 1960] and alternative views of the biology of cognition [Maturana 1970]. There is growing recognition of the value of a "science of subjectivity" that encompasses both objective and subjective interactions, including conversation [Pask 1976]. Designers are rediscovering the influence of cybernetics on the tradition of 20th-century design methods, and the need for rigorous models of goals, interaction, and system limitations for the successful development of complex products and services, such as those delivered via today's software networks. And, as in any social cycle, students of history reach back with minds more open than was possible at the inception of cybernetics, to reinterpret the meaning and contribution of a previous era.
2. Artificial Intelligence and Cybernetics:
Artificial Intelligence and Cybernetics: Aren't they the same thing Isn't one just about computers and the other about robots The answer to these questions is emphatically, No.
Artificial Intelligence Cybernetics
1. AI theories are more concerned with general-purpose techniques of problem solving. The underlying feedback mechanism does not play a central role.
2. Organisms map external objects to internal states.
3. Nervous systems store the information.
4. Truth exists in the world.
5. Intelligence resides in the manipulation of information. 1. A cybernetic normally focuses on a class of controlled objects. The feedback mechanism from a controlled object to the corresponding controller plays a key role in synthesizing
2. Organisms map through an environment back onto themselves.
3. Nervous system reproduces adaptive relationships.
4. Social agreement is primary objectivity.
5. Intelligent resides in observed conversations.
The field of Artificial Intelligence (AI) came into being when concepts of universal computation, the brain as computer, and digital computing were combined. Fundamentally concerned with demonstration and application, AI moved inevitably to assume the challenge of reproducing and/or explaining human mentation, problem solving and language. It stands today as a major influence on the fields of biology, cognitive and computation science, and epistemology.
The field of Cybernetics came into being when concepts of information, feedback and control were generalized from specific applications to systems in general, including systems of living organisms, systems of self-reference and systems of language. Fundamentally an applied philosophy, cybernetics has taken on problems of subjectivity in science while still addressing how to make intelligent artifacts. It stands today as a major influence on biology, cognitive and computation science, and epistemology.
Artificial Intelligence (AI) takes for granted that knowledge is a commodity that can be stored inside of a machine, and that the application of such stored knowledge to the real world constitutes intelligence.
In contrast, cybernetics uses epistemology (the study of how we know what we know) to understand the workings and limitations of the media and environments (technological, biological, or social) within which we live, to develop useful descriptions leading to effective management. Cybernetic descriptions of psychology, language, arts, performance, or intelligence (to name a few) may be quite different from more conventional, hard "scientific" views.
AI's "realist" perspective assumes and accepts the world-as-it-is. The cybernetic perspective is "constructivist", and sees that the world of human civilization is created by an intelligence acting in a social tradition.
Ironically but logically, AI and Cybernetics have each gone in and out of fashion and influence. Cybernetics started a bit in advance of AI, but AI has dominated for the last 25 years. Now recent difficulties in AI have led to renewed search for solutions that mirror the past approaches of Cybernetics.
3. Approach of cybernetic to AI:
There is no established unifying theory or paradigm that guides AI research. Researchers disagree about many issues. A few of the most long standing questions that have remained unanswered are these: Can intelligence be reproduced using high-level symbols, similar to words and ideas Or does it require "sub-symbolic" processingShould artificial intelligence simulate natural intelligence, by studying human psychology or animal neurobiology Or is human biology as irrelevant to AI research as bird biology is to aeronautical engineering Can intelligent behavior be described using simple, elegant principles (such as logic or optimization) Or does artificial intelligence necessarily require solving many unrelated problems
AI is predicated on the presumption that knowledge is a commodity that can be stored inside of a machine, and that the application of such stored knowledge to the real world constitutes intelligence [Minsky 1968]. Only within such a "realist" view of the world can, for example, semantic networks and rule-based expert systems appear to be a route to intelligent machines. Cybernetics in contrast has evolved from a "constructivist" view of the world [von Glasersfeld 1987] where objectivity derives from shared agreement about meaning, and where information (or intelligence for that matter) is an attribute of an interaction rather than a commodity stored in a computer [Winograd & Flores 1986]. These differences are not merely semantic in character, but rather determine fundamentally the source and direction of research performed from a cybernetic, versus an AI, stance.
Winograd and Flores credit the influence of Humberto Maturana, a biologist who recasts the concepts of "language" and "living system" with a cybernetic eye [Maturana & Varela 1988], in shifting their opinions away from the AI perspective. They quote Maturana: "Learning is not a process of accumulation of representations of the environment; it is a continuous process of transformation of behavior through continuous change in the capacity of the nervous system to synthesize it. Recall does not depend on the indefinite retention of a structural invariant that represents an entity (an idea, image or symbol), but on the functional ability of the system to create, when certain recurrent demands are given, a behavior that satisfies the recurrent demands or that the observer would class as a reenacting of a previous one." [Maturana 1980] Cybernetics has directly affected software for intelligent training, knowledge representation, cognitive modeling, computer-supported coÃƒÂ¶perative work, and neural modeling. Useful results have been demonstrated in all these areas. Like AI, however, cybernetics has not produced recognizable solutions to the machine intelligence problem, not at least for domains considered complex in the metrics of symbolic processing. Many beguiling artifacts have been produced with an appeal more familiar in an entertainment medium or to organic life than a piece of software [Pask 1971]. Meantime, in a repetition of history in the 1950s, the influence of cybernetics is felt throughout the hard and soft sciences, as well as in AI. This time however it is cybernetics' epistemological stance â€ that all human knowing is constrained by our perceptions and our beliefs, and hence is subjective â€ that is its contribution to these fields. We must continue to wait to see if cybernetics leads to breakthroughs in the construction of intelligent artifacts of the complexity of a nervous system, or a brain.
4. Cybernetics and brain simulation
FIG 5.4.1.Brain Simulation
The human brain provides inspiration for artificial intelligence researchers, however there is no consensus on how closely it should be simulated.
In the 40s and 50s, a number of researchers explored the connection between neurology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter's turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton University and the Ratio Club in England.
Chapter 6: Software Cybernetics
1. Need of Software Cybernetics:
Certainly, the introduction and utilization of the notion of computability and the advent of software technology constitute one of the great achievements of scientific human history. Computing and software technologies significantly impact economical development, the entertainment industry, and scientific activities everywhere. Indeed, the current age of information depends critically on computing and software technologies. This argument is further strengthened by the establishment of various inter-disciplinary areas such as computing mathematics, computational fluid mechanics, and computer simulation and by the new and growing internet-based areas of e-commerce virtual laboratories and e-science. This essential importance of software technologies raises a number of fundamental questions: What computable specifications or services can software systems implement What is the nature of software behavior How can software behavior be formalized How dependable are software systems. The emerging inter disciplinary area of software cybernetics is motivated by the fundamental question of whether or not and how software behavior can be controlled. Software behavior includes the behavior of software development processes, software maintenance processes, software evolution processes as well as that of software execution itself.
2. Software Cybernetics Concepts and Definitions:
The area of software cybernetics explores the interplay between software processes and control. Nobert Wienerâ„¢s Cybernetics is concerned with control and communication in the animal and the machine.
If we treat software as a part of the machine, then a simple interpretation of the term software cybernetics that it is concerned with the co
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