scholarly journals ПРИНЦИП ФУНКЦІОНАЛЬНОЇ САМООРГАНІЗАЦІЇ ДІЯЛЬНОСТІ ІНТЕЛЕКТУАЛЬНИХ СИСТЕМ

2019 ◽  
pp. 18-28
Author(s):  
Серій Ілліч Доценко
Keyword(s):  
Control Systems ◽  
Intelligent Systems ◽  
Feedback Loop ◽  
Intelligent System ◽  
Functional System ◽  
Self Organization ◽  
Main Attention ◽  
Functional Systems ◽  
Intellectual System ◽  
Smart Things

The five principles of self-organization of cybernetic systems are formed in classical cybernetics in the form of two hypotheses of N. Wiener and three hypotheses of W. R. Ashby. The main attention in the development of the theory of a functional system is given to its analysis as an integral unit, and the formation on its basis of the theories of intelligent systems. At the same time, no attention was left to the study of the principle of the mechanism for ensuring compliance with the result obtained and the project established for it. The conformity mechanism, which is formed as part of a functional system, is implemented on the basis of the principle of self-organization of the functional system’s activity at the stage of a future result project’s implementation through double sequential feedback through the “Action Results Acceptor” mechanism. Based on this principle, it is possible to formulate the law of self-organization of an intellectual system in the following form. For functional self-organization of an intelligent system based on a mechanism to ensure compliance with the result of an activity and its project, it is necessary to include an “Acceptor of an action result” in the feedback loop to match the result of an action, a project of a future result of an action, and a management team. The principles of self-organization formed in classical cybernetics turned out to be elements of the clarified single principle of the self-organization of functional systems activity. In this work, it was realized that the meaning of knowledge about the functional systems in the theory and the theory of dialogue control systems of two successive feedback loops and the mechanism of their combination in the “Acceptor of the results of action” was realized. It is thanks to these contours that the principle of functional self-organization of activities is implemented, the founders of classical cybernetics so stubbornly sought and from which they abandoned technical cybernetics. The task of the formation of the goal of the activity can be solved by knowing the mechanism of the formation of the project of a future result based on heuristic self-organization for physiological and cybernetic systems. The solution to this problem will ensure the formation of "smart things" in Industry 5.0. After all, “smart things” should be “intelligent”

Some Kinds of Pseudo-Intelligent Control Systems for Selecting Trajectories of Flight Vehicles

2014 ◽  
Vol 598 ◽  
pp. 557-561 ◽  
Author(s):  
A.V. Proletarsky ◽  
K.A. Neusipin ◽  
Kai Shen
Keyword(s):  
Control Systems ◽  
Intelligent Control ◽  
Intelligent Systems ◽  
Self Organization ◽  
Functional Systems ◽  
Intelligent Control Systems ◽  
Flight Vehicles

In order to effectively solve the problem of selection trajectories of flight vehicles, some pseudo-intelligent systems with functional structures were established. Moreover, systemogenesis of pseudo-intelligent control systems of flight vehicles was illustrated. Based upon the theory of P.K. Anoxin functional systems and self-organization algorithms, the perspective pseudo-intelligent control systems for all stages of systemogenesis can make a reality of the whole-life control of flight vehicles.

scholarly journals ІНТЕЛЕКТУАЛЬНІ СИСТЕМИ: ПРИНЦИП ЕВРИСТИЧНОЇ САМООРГАНІЗАЦІЇ

2020 ◽  
pp. 4-16
Author(s):  
Серій Ілліч Доценко
Keyword(s):  
Control Systems ◽  
Intelligent Systems ◽  
Production Control ◽  
Self Organization ◽  
Automated Control ◽  
Intellectual System ◽  
The Future ◽  
Production Control Systems ◽  
Characteristic Features ◽  
Theory Of Functional Systems

The purpose of this study is to compare methods of self-organization for two forms of cybernetic systems, namely: intelligent systems based on the theory of functional systems, as organized whole; automated control systems. Each of these systems can be divided into two parts. Moreover, for intelligent systems, the problem of self-organization is posed as the problem of determining the principle of combining the selected parts into an organized whole. It has been established that the principle of such a combination is the dialectical connection between the results of the tasks being solved in each of the parts. The dialectical connection is realized in the form of a dialectical unity of the concepts of “general” and “single”. It is proposed to consider this principle of combining parts of the intellectual system as the principle of heuristic dialectic self-organization. At the same time, automated control systems are characterized by the division of the system into two parts, namely: the human operator; management object. However, with this approach, each of these parts is considered separately. Therefore, for each of the parts it is proposed to determine its own principle of self-organization. In the course of the study, it was proposed to move on to establishing the principle of self-organization for parts of the intellectual system. At the same time, it is proposed to change the method for solving this problem. If for automated systems it is proposed to first determine the characteristic signs of activity and to propose appropriate heuristics for their processing, then for intelligent systems it is proposed to recognize the principle of heuristic self-organization as a dialectical unity of concepts. The principle of dialectical unity of the concepts of “common” and “single” is proposed to be used to study the mechanisms of self-organization of activities to solve problems in the relevant parts of the intellectual system. The first part of the intellectual system that solves the problem of implementing the established project of the future result is technological activity. An important circumstance, this activity is also divided by us into organizational and technological. It is clear that any process should be organized. Internally. Since we have already chosen the principle of heuristic self-organization, it remains to establish the characteristic features for this form of activity. To reveal the content of factors for this form of activity, we have chosen the concept of “process” and “resource”. Based on this, four forms of factors were established, and dialectic pairs of these factors were formed, for which a model architecture was established for the factor representation of the project of the future result of activity. Studying the technological activities for the implementation of the established project of the future result, we actually solved the problem of forming a model for the project of the future result, which is the result of solving the first problem and is the basis for solving the second problem. It should also be noted that the development of intelligent production control systems for Industry 4.0 is impossible outside the theory of intelligent systems, which in turn is based on the principles of heuristic self-organization.

Artificial intelligence as a moving force of improvement and innovative development in education and pedagogic

2019 ◽  
pp. 21-30
Author(s):  
M. G. Koliada ◽  
T. I. Bugayova
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Interaction Analysis ◽  
Educational Data Mining ◽  
Main Task ◽  
Intellectual System ◽  
Will Force ◽  
To Come ◽  
Artificial Intelligence Systems

The article discusses the history of the development of the problem of using artificial intelligence systems in education and pedagogic. Two directions of its development are shown: “Computational Pedagogic” and “Educational Data Mining”, in which poorly studied aspects of the internal mechanisms of functioning of artificial intelligence systems in this field of activity are revealed. The main task is a problem of interface of a kernel of the system with blocks of pedagogical and thematic databases, as well as with the blocks of pedagogical diagnostics of a student and a teacher. The role of the pedagogical diagnosis as evident reflection of the complex influence of factors and reasons is shown. It provides the intelligent system with operative and reliable information on how various reasons intertwine in the interaction, which of them are dangerous at present, where recession of characteristics of efficiency is planned. All components of the teaching and educational system are subject to diagnosis; without it, it is impossible to own any pedagogical situation optimum. The means in obtaining information about students, as well as the “mechanisms” of work of intelligent systems based on innovative ideas of advanced pedagogical experience in diagnostics of the professionalism of a teacher, are considered. Ways of realization of skill of the teacher on the basis of the ideas developed by the American scientists are shown. Among them, the approaches of researchers D. Rajonz and U. Bronfenbrenner who put at the forefront the teacher’s attitude towards students, their views, intellectual and emotional characteristics are allocated. An assessment of the teacher’s work according to N. Flanders’s system, in the form of the so-called “The Interaction Analysis”, through the mechanism of fixing such elements as: the verbal behavior of the teacher, events at the lesson and their sequence is also proposed. A system for assessing the professionalism of a teacher according to B. O. Smith and M. O. Meux is examined — through the study of the logic of teaching, using logical operations at the lesson. Samples of forms of external communication of the intellectual system with the learning environment are given. It is indicated that the conclusion of the found productive solutions can have the most acceptable and comfortable form both for students and for the teacher in the form of three approaches. The first shows that artificial intelligence in this area can be represented in the form of robotized being in the shape of a person; the second indicates that it is enough to confine oneself only to specially organized input-output systems for targeted transmission of effective methodological recommendations and instructions to both students and teachers; the third demonstrates that life will force one to come up with completely new hybrid forms of interaction between both sides in the form of interactive educational environments, to some extent resembling the educational spaces of virtual reality.

scholarly journals ПРИНЦИП ЦІЛІСНОЇ ОРГАНІЗАЦІЇ ІНТЕЛЕКТУАЛЬНИХ СИСТЕМ

2019 ◽  
pp. 4-16
Author(s):  
Серій Ілліч Доценко
Keyword(s):  
Control System ◽  
Intelligent Systems ◽  
Systems Approach ◽  
Intelligent System ◽  
Control Object ◽  
The Other ◽  
Methodological Basis ◽  
Other Hand ◽  
Intellectual System ◽  
The Impact

The antinomy of the division of the intellectual system into parts has been formed, namely: the intellectual system is an organized whole, which is formed from at least two parts; for an intelligent system, as an organized whole, it is impossible to divide into a controlling part (control system) and a part of which is controlled. It has been established that the antinomy of dividing an intelligent system into parts is generated by the fact that, traditionally, the control system and the control object are considered separately. Therefore, it is considered the system, and not an organized whole. The role of the theory of functional systems in the development of cybernetic systems as intellectual systems is defined. This theory is the basis for the development of intelligent systems A. V. Chechkinim, K. A. Pupkov, and other authors. On the other hand, M. I. Meltzer develops the theory of dialogue systems for managing production enterprises, the basis of which is the mathematical theory of systems. It is shown that the functional representation architectures for these systems are similar. The similarity is determined on the basis of the task approach. On the one hand, there is a mutual non-recognition of the results of scientific schools of physical and technical cybernetics, and on the other hand, there is a similarity of the results obtained. It has been established that the methodological basis of the holistic approach is the task approach to the formation of a solving system, developed in the theory of dialogue management of production. To do this, it is necessary to include the “Activity to get the result” block in the solving system in order to turn it into an intellectual system. The methodological basis of a systems approach is a functional approach to the formation of systems. The main lesson of the classical cybernetics crisis, regarding the organizational principle for two parts of an organized whole, is to establish a dialectical unity of concepts in the form of a “general” concept and a “concrete” concept for problem-solving results in the control system and control object. Thus, a dialectically organized whole is formed. The article also analyzes the impact of the study of intelligent systems on the development of the methodological foundations of the Industry 4.0 platform. The next task that needs to be solved is the formation of the principle of functional self-organization, which is the basis for the formation of a mechanism for ensuring consistency between the results of solving problems in parts of a dialectically organized whole

scholarly journals Linguistic Ontologies: Designing and Using in the Educational Intellectual Systems

2021 ◽  
Vol 4 (1) ◽  
pp. 97-111
Author(s):  
Olha Tkachenko ◽  
Kostiantyn Tkachenko ◽  
Oleksandr Tkachenko
Keyword(s):  
Computer Networks ◽  
Intelligent Systems ◽  
Semantic Analysis ◽  
Intelligent System ◽  
Subject Area ◽  
Intellectual System ◽  
Proposed Model ◽  
Small Set ◽  
Ontological Model ◽  
Linguistic Ontology

The purpose of the article is to investigate and consider the general trends, problems and prospects of designing and using linguistic ontologies in educational intellectual systems. The research methodology consists in semantic analysis methods of the basic concepts in the considered subject area (linguistic ontologies in the educational intellectual systems). The article discusses approaches to the use of linguistic models in modern educational intelligent systems. The novelty of the research is the analysis of the linguistic ontologies use in the educational intellectual systems. Conclusions. A model of linguistic ontology for the domain (disciplines “Computer Networks” and “Modelling Systems”) is presented. This model is used in the development of an educational intellectual system that supports online learning in these disciplines. The proposed model describes a set of relations of linguistic ontology, specially selected to describe the analyzed domain. To ensure these properties, it was proposed to use a small set of relationships. The proposed linguistic ontological model is implemented in an educational intelligent system that supports such disciplines as “Computer Networks” and “Modelling Systems”.

scholarly journals Didactic model of development of research activities of schoolchildren in a hybrid intellectual learning environment

2021 ◽  
Vol 50 (2) ◽  
pp. 278-292
Author(s):  
Svetlana N. Dvoryatkina ◽  
◽  
Larisa V. Zhuk ◽  
Eugeny I. Smirnov ◽  
Sergey V. Shcherbatykh ◽  
...  
Keyword(s):  
Learning Environment ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Educational Process ◽  
Self Organization ◽  
Research Activity ◽  
Hybrid Intelligent Systems ◽  
Research Activities ◽  
Intelligent Management ◽  
The Individual

Introduction. The problem of developing and implementing intelligent digital technologies in the system of teaching subject areas of knowledge for the purpose of self-organization of the individual, understanding and comprehending complex mathematical knowledge as a principle of personal development based on individualization of learning, establishing personalized and computerized feedback of cognitive and evaluative processes is relevant and far from solved. In this problem field, the issues of organizing the research activities of students in the process of adapting modern achievements in science to school mathematics through intellectual management are updated. Materials and methods. To identify new trends in the development of the concept of "research activity", methods of theoretical and methodological analysis (comparative, retrospective, modeling), analysis of mathematical, psychological, pedagogical, technical literature, methods of systematization of existing experience on the research problem were used. The possibilities of methods of intelligent management, design of a hybrid intellectual learning environment for creating a model of research activity are studied. The results of the study. A didactic model of research activity is developed based on the use of hybrid intelligent systems in the course of adapting modern scientific achievements with the manifestation of the effects of self-organization of the individual. The author's concept of intelligent management of the educational process in a hybrid intellectual learning environment was the structure-forming module of the model construction. The system of principles considered in the context of the unity and mutual influence of the three components of the structure of the information and educational space is formulated and justified: a set of digital educational platforms and technologies; digital interaction infrastructure in the context of the interactive triad "teacher-computer-student" of the educational process; digital information and educational content. In the course of mastering complex knowledge through historiogenesis and the range of its applications, technological constructs of clusters of foundation components of the generalized structure are built in the direction of building individual educational trajectories of schoolchildren using hybrid neural networks that make up the content component of the research activity development model. Conclusion. The results of the study are of practical value, as they serve as a methodological basis for creating a software package that implements the functionality of a hybrid intelligent system for developing students' research activities based on the developed principles and structural components using expert systems and fuzzy modeling.

scholarly journals COUNTERFACTUAL TEMPORAL MODEL OF CAUSAL RELATIONSHIPS FOR CONSTRUCTING EXPLANATIONS IN INTELLIGENT SYSTEMS

2021 ◽  
pp. 41-46
Author(s):  
Serhii Chalyi ◽  
Volodymyr Leshchynskyi ◽  
Irina Leshchynska
Keyword(s):  
Information System ◽  
Intelligent Systems ◽  
Intelligent System ◽  
System Development ◽  
Causal Relationships ◽  
Temporal Properties ◽  
Intellectual System ◽  
Transitivity Property ◽  
Temporal Model ◽  
Intelligent Information

The subject of the research is the processes of constructing explanations based on causal relationships between states or actions of an intellectualsystem. An explanation is knowledge about the sequence of causes and effects that determine the process and result of an intelligent informationsystem. The aim of the work is to develop a counterfactual temporal model of cause-and-effect relationships as part of an explanation of the process offunctioning of an intelligent system in order to ensure the identification of causal dependencies based on the analysis of the logs of the behavior ofsuch a system. To achieve the stated goals, the following tasks are solved: determination of the temporal properties of the counterfactual description ofcause-and-effect relationships between actions or states of an intelligent information system; development of a temporal model of causal connections,taking into account both the facts of occurrence of events in the intellectual system, and the possibility of occurrence of events that do not affect theformation of the current decision. Conclusions. The structuring of the temporal properties of causal links for pairs of events that occur sequentially intime or have intermediate events is performed. Such relationships are represented by alternative causal relationships using the temporal operators"Next" and "Future", which allows realizing a counterfactual approach to the representation of causality. A counterfactual temporal model of causalrelationships is proposed, which determines deterministic causal relationships for pairs of consecutive events and pairs of events between which thereare other events, which determines the transitivity property of such dependencies and, accordingly, creates conditions for describing the sequence ofcauses and effects as part of the explanation in intelligent system with a given degree of detail The model provides the ability to determine cause-andeffect relationships, between which there are intermediate events that do not affect the final result of the intelligent information system.

scholarly journals Development of the structure of an intelligent locomotive DSS and as-sessment of its efectiveness

2020 ◽  
Vol 56 (4) ◽  
pp. 47-58
Author(s):  
Oleksandr Gorobchenko ◽  
Oleksandr Nevedrov
Keyword(s):  
Quality Control ◽  
Decision Support ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Complex Structure ◽  
Control Process ◽  
Great Distance ◽  
Train Control ◽  
Intellectual System

The purpose of the article is developing the locomotive structure of intellectual system of support of decision-making and to find a criterion by which to adequately assess different control action to the train. System of decision support for locomotive crew is seen as a complex structure with complex interactions located at a great distance, on-board locomotive systems. The quality of the organization determines the effectiveness of the system as a whole. To solve the problem of creating the optimal structure of the DSS applies the aggregate-decomposition method that involves two steps: decomposition of the problem into a number of subproblems and aggregating the partial results. To evaluate the quality control of a locomotive used the concept of control strategy with specific indicators. Design is developed and structure of locomotive DSS is obtained, taking into account peculiarities of operation of railway transport. To ac-count for not only quantitative but also qualitative characteristics of activity of the locomotive or intellectual systems of decision support, it is proposed to use methods of fuzzy logic. So were able to deduce and calculate the additive criterion of the quality control activities of the intelligent system. Formal indicator of the quality of the train control process using different strategies is received. In the work theoretically grounded definition of the weighting factors for each partial criterion of the quality of train control. Using the dependencies derived, the nature of the influence of the value of partial criteria on the quality of train control in relation to a strategy. The results of the work allow to more accurately simulate the operations of a locomotive crew, which in the future will serve as the basis for the development of autonomous intelligent systems of locomotive control. The developed method is shown to be three main criteria which values the safety, energy consumption, and execution time schedule. However, for more flexible and accurate model, this approach allows to enter additional criteria, and the simplicity of the calculation provides the necessary speed when implemented on on-board locomotive computers.

The Central Role of Cognitive Computations in Human-Information Interaction

2018 ◽  
Author(s):  
Wai-Tat Fu ◽  
Jessie Chin ◽  
Q. Vera Liao
Keyword(s):  
Information Systems ◽  
Cognitive Science ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Levels Of Abstraction ◽  
Intelligent Information Systems ◽  
Emergent Behaviour ◽  
Information Interaction ◽  
Intelligent Information

Cognitive science is a science of intelligent systems. This chapter proposes that cognitive science can provide useful perspectives for research on technology-mediated human-information interaction (HII) when HII is cast as emergent behaviour of a coupled intelligent system. It starts with a review of a few foundational concepts related to cognitive computations and how they can be applied to understand the nature of HII. It discusses several important properties of a coupled cognitive system and their implication to designs of information systems. Finally, it covers how levels of abstraction have been useful for cognitive science, and how these levels can inform design of intelligent information systems that are more compatible with human cognitive computations.

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