scholarly journals User interfaces ontology in the cybernetic model of intelligent sys-tems

2021 ◽  
Vol 11 (1) ◽  
pp. 89-103
Author(s):  
K.I. Kostenko ◽  
◽  
V.Yu. Belkin
Keyword(s):  
User Interfaces ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Three Dimensional ◽  
Subject Area ◽  
Knowledge Flows ◽  
Professional Activity ◽  
Information Structures ◽  
Knowledge Processing ◽  
Abstract Knowledge

A model of an intelligent system is proposed for implementing the control and professional use of a knowledge collec-tions related to subject area of software interfaces with external users. The basis of formalization is an abstract model of an intelligent system. It is based on coordinated and balanced invariants of the knowledge representation formalism model. Invariants set includes classes of morphisms for abstract knowledge processing modelling with morphisms' do-mains, invariants of the multidimensional architecture of intelligent systems components, which includes inter-component knowledge flows and processes of knowledge synthesis within components, as well as invariants of control agents for abstract knowledge flows and processes within intelligent systems. The intelligent system knowledge base is presented as subject area ontology by non-ordered series of simple knowledge. Fragments of such ontology are distrib-uted between components of intelligent systems three-dimensional architecture. The basis of such structure based on the dimensions of abstraction, structuring and the level of knowledge addressed to and processed within intelligent sys-tem's separate components. The ontologies reflect the ideas about the structures of memory and the processes of think-ing used to model the schemes of the professional activity of a specialist. The basis for the formalization of such con-cepts is the fundamental principles of philosophy, linguistics, cognitive psychology, mathematics, and system engineer-ing. This allows deploying a comprehensive system of classes of information structures and processes for complex knowledge synthesizing that support achievement of various cognitive goals implemented by specialists within profes-sional tasks implementation processes. The goals system includes extraction, analysis and application of knowledge about user interfaces. The variety of such goals is modeled by a high-level implementation pattern system. They are composed of basic types of goals and are implemented using knowledge processing cognitive operations. Knowledge structures in the format of semantic hierarchies are used as a unified representation of knowledge. Cognitive goals are realized by combinations of operations on structured knowledge, adapted to special classes of structures.

The hybrid intelligent systems design using grey systems theory

2015 ◽  
Vol 5 (2) ◽  
pp. 194-205 ◽  
Author(s):  
Scarlat Emil ◽  
Virginia Mărăcine
Keyword(s):  
Systems Theory ◽  
Intelligent Systems ◽  
Explicit Knowledge ◽  
Intelligent System ◽  
Grey System Theory ◽  
Knowledge Flows ◽  
Content Type ◽  
Hybrid Intelligent Systems ◽  
Advantages And Disadvantages ◽  
Grey Systems Theory

Purpose – The purpose of this paper is to discuss how tacit and explicit knowledge determine grey knowledge and how these are stimulated through interactions within networks, forming the grey hybrid intelligent systems (HISs). The feedback processes and mechanisms between internal and external knowledge determine the apparition of grey knowledge into an intelligent system (IS). The extension of ISs is determined by the ubiquity of the internet but, in our framework, the grey knowledge flows assure the viability and effectiveness of these systems. Design/methodology/approach – Some characteristics of the Hybrid Intelligent Knowledge Systems are put forward along with a series of models of hybrid computational intelligence architectures. More, relevant examples from the literature related to the hybrid systems architectures are presented, underlying their main advantages and disadvantages. Findings – Due to the lack of a common framework it remains often difficult to compare the various HISs conceptually and evaluate their performance comparatively. Different applications in different areas are needed for establishing the best combinations between models that are designed using grey, fuzzy, neural network, genetic, evolutionist and other methods. But all these systems are knowledge dependent, the main flow that is used in all parts of every kind of system being the knowledge. Grey knowledge is an important part of the real systems and the study of its proprieties using the methods and techniques of grey system theory remains an important direction of the researches. Originality/value – The paper discusses the differences among the three types of knowledge and how they and the grey systems theory can be used in different hybrid architectures.

Ontologies' Closures Modeling by Formalisms of Semantic Hierarchies

2020 ◽  
Vol 11 (6) ◽  
pp. 349-360
Author(s):  
K. I. Kostenko ◽  
Keyword(s):  
Intelligent Systems ◽  
Life Cycles ◽  
Knowledge Flows ◽  
Knowledge Structures ◽  
Uniform Structure ◽  
Algebraic Structures ◽  
Knowledge Processing ◽  
Special Operations ◽  
Knowledge Presentation ◽  
Semantic Hierarchies

Schemes are studied for modeling the complex knowledge structures as synthesized from knowledge areas ontologies elements. These structures applications relate to knowledge life cycles and knowledge flows stages within intelligent systems. The format of semantic hierarchies is proposed as unified and universal for the synthesis of these structures. This allows replacing the general case of knowledge algebraic structures by special case of semantic hierarchies. Constructing the synthesized knowledge structures is performed by special operations knowledge alge­braic structures in any knowledge representation formalisms. These operations simulate fundamental mathematical systems functional aspects. They are adapted to the knowledge formalisms attributes. Attributes are explored within different knowledge areas. They associated generally with thinking operations and mind memory structure. Datasets (operations bases) of synthesis processes operations are constructed as special classes of knowledge with a uniform structure. Ontologies for considered knowledge areas are used as source of such bases constructing. Ontologies closures are defined as sets of knowledge that may be constructed off ontologies elements by synthesis operations. They determine the ontologies expressive capabilities. The constructs and operations over ontologies, that proposed at descriptive logics, can be modelled by knowledge complete structural representations, adopted for semantic hierarchies formalisms. Such formalisms are convenient for simulating knowledge presentation and processing. They form foundation for constructing intelligent systems abstract and applied models.. The possibility is proved for trans­ferring the knowledge properties and knowledge processing schemes at semantic hierarchies to the general case of knowledge algebraic structures. The schemes for modeling the ontological constructions as semantic hierarchies are given. This proves possibility of applying such formalisms as the basis for modeling synthesis processes in ontolo­gies. Such schemes allow constructing the ontologies closures as generated by knowledge-processing operations sequences. Last fact means possibility for formalisms of semantic hierarchies to be uniform foundation of modelling the knowledge flows and knowledge transforming processes by intelligent systems.

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”.

Core Invariants of the Mathematical Model of an Intelligent System

2021 ◽  
Vol 12 (3) ◽  
pp. 157-168
Author(s):  
K. I. Kostenko ◽  
Keyword(s):  
Mathematical Model ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Subsequent Development ◽  
Knowledge Flows ◽  
Fundamental Model ◽  
Knowledge Presentation ◽  
Thinking Processes ◽  
The Mathematical Model ◽  
Holistic Description

A holistic description of a universal mathematical model for the concept of an intelligent system is given. It is based on formalized invariants associated with the processes of creating and applying such systems. The core of the model is formed by consistent descriptions for sections of knowledge formalisms, components of multidimensional architecture and knowledge flows processes within it, as well as cybernetic hierarchical multi agents systems that control the intelligent systems subjective existence. The fundamental invariants of the knowledge presentation and processing are directly implemented by these main sections basic elements. Invariants form unified set of intelligent systems general attributes. This set allows carrying out comprehensive formal modelling of the intelligence. These invariants are associated with knowledge aspects. They are developed and used at knowledge areas that deal with exploring the memory structural organization and thinking processes models. The tools for transforming the proposed abstract model into the models of specific intelligent systems are morphisms of homomorphic expansion. These morphisms concretize the content of the main structural and functional elements of the intelligent system fundamental model. At the same time, the varieties of entities implemented by model elements are narrowed to the families of objects that make up applied intelligent systems. These systems inherit the properties of fundamental model common elements. Intermediate models of the processes of converting the original model into applied ones allow studying these models properties by mathematical tools. Intermediate models form the basis for the subsequent development of the technology of creating and applying multilevel intelligent systems.

scholarly journals Intelligent Systems for Active Program Diagnosis

2000 ◽  
Vol 5 ◽  
pp. 157
Author(s):  
Haider Ali Ramadhan ◽  
Khalil Shihab
Keyword(s):  
User Interfaces ◽  
Program Analysis ◽  
Intelligent Systems ◽  
Domain Knowledge ◽  
Intelligent System ◽  
Entire Solution ◽  
Design Considerations ◽  
Primary Means ◽  
Problem Solving Process ◽  
Representation Techniques

 Intelligent program diagnosis systems are computer programs capable of analyzing logical and design-level errors and misconceptions in programs. Upon discovering the errors, these systems provide intelligent feedback and thus guide the users in the problem-solving process. Intelligent program diagnosis systems are classified by their primary means of program analysis. The most distinct split is between those systems that are unable to analyze partial code segments as they are provided by the user and must wait until the entire solution code is completed before attempting any diagnosis, and those that are capable of analyzing partial solutions and providing proper guidance whenever an error or misconception is encountered. This paper gives an overview of the field and then critically compares work accomplished on several closely related active diagnosis systems, emphasizing such issues as the representation techniques used to capture the domain knowledge required for the diagnosis, ability to handle the diagnosis of partial code segments of the solutions, features of the user interfaces, and methodologies used in conducting the diagnosis process. Finally the paper presents a detailed discussion on issues related to active program diagnosis along with various design considerations to improve the engineering of this approach to intelligent diagnosis. The discussion presented in this paper tackles the issues referred above within the context of DISCOVER, an intelligent system for programming by discovery.

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.

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.

scholarly journals Theoretical, Measured, and Subjective Responsibility in Aided Decision Making

2021 ◽  
Vol 11 (1) ◽  
pp. 1-37
Author(s):  
Nir Douer ◽  
Joachim Meyer
Keyword(s):  
Intelligent Systems ◽  
Intelligent System ◽  
Classification Systems ◽  
Strongly Correlated ◽  
Human Capabilities ◽  
Legal Decisions ◽  
Subjective Perceptions ◽  
Causal Responsibility ◽  
Quantification Model ◽  
Human Responsibility

When humans interact with intelligent systems, their causal responsibility for outcomes becomes equivocal. We analyze the descriptive abilities of a newly developed responsibility quantification model (ResQu) to predict actual human responsibility and perceptions of responsibility in the interaction with intelligent systems. In two laboratory experiments, participants performed a classification task. They were aided by classification systems with different capabilities. We compared the predicted theoretical responsibility values to the actual measured responsibility participants took on and to their subjective rankings of responsibility. The model predictions were strongly correlated with both measured and subjective responsibility. Participants’ behavior with each system was influenced by the system and human capabilities, but also by the subjective perceptions of these capabilities and the perception of the participant's own contribution. A bias existed only when participants with poor classification capabilities relied less than optimally on a system that had superior classification capabilities and assumed higher-than-optimal responsibility. The study implies that when humans interact with advanced intelligent systems, with capabilities that greatly exceed their own, their comparative causal responsibility will be small, even if formally the human is assigned major roles. Simply putting a human into the loop does not ensure that the human will meaningfully contribute to the outcomes. The results demonstrate the descriptive value of the ResQu model to predict behavior and perceptions of responsibility by considering the characteristics of the human, the intelligent system, the environment, and some systematic behavioral biases. The ResQu model is a new quantitative method that can be used in system design and can guide policy and legal decisions regarding human responsibility in events involving intelligent systems.

scholarly journals Diagnostics of psychological readiness of high school students for research activities in mathematics in the design context of a hybrid intellectual learning environment

2021 ◽  
Vol 54 (6) ◽  
pp. 192-210
Author(s):  
Svetlana N. Dvoryatkina ◽  
◽  
Vera S. Merenkova ◽  
Eugeny I. Smirnov ◽  
◽  
...  
Keyword(s):  
High School Students ◽  
Learning Environment ◽  
Boundary Conditions ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Educational Institution ◽  
Value Orientations ◽  
School Students ◽  
Research Activities ◽  
Scientific Potential

Introduction. The problem of improving the process of organizing and supporting the project and research activities of schoolchildren through intelligent management for the purpose of self-organization of the individual, understanding and comprehending complex mathematical knowledge as a principle of personal development is relevant and far from solved. Intelligent systems provide the process of individualization of learning, the establishment of personalized and computerized feedback of cognitive and creative processes. The purpose of the article is to assess the student's readiness for research activities in the context of designing a hybrid intelligent learning environment. Materials and methods. The assessment of the student's psychological readiness for research activities in the conditions of using a hybrid intellectual environment was carried out on an experimental representative sample of students of 1-2 courses of secondary vocational education (n1=42) and students of the senior classes of secondary schools (n2=30). The diagnosis was carried out using the intelligence structure test of R. Amthauer, the creativity questionnaire of D. Johnson, the test "Individual styles of thinking" by A. Alekseev, L. Gromova, the methods of value orientations by M. Rokich, etc. The significance of the differences was established by means of Student's t-test, Fisher's angular transformation, χ2-test. The results of the study. The assessment of psychological readiness for research activities in mathematics was carried out on the basis of the developed nine parameters of scientific potential. The presented results allow us to pre-set the framework of boundary conditions in order to minimize the imprinting time of a hybrid intelligent system (including the selection of the neural network topology). For all three groups of criteria, differences by gender were established, for example, by the parameter "value orientations" (temp  = 2.26 > tcr = 2.02); by the parameter "creativity" (χemp2 = 6,02 ≥ χcr2 (0,05;2) = 5,99). And also by the type of educational institution, for example, by the parameter “motivation to achieve the result” (φemp = 0,186 > φcr = 1,64). Conclusion. The results of the research are of practical value, as they serve as a technological basis for establishing the boundaries and boundary conditions of the most significant parameters for the effective realization of scientific potential, expressed in the work of a specialized web interface created with the student's personal account.

scholarly journals INTERACTIVE VISUALIZATION TECHNOLOGY IN AUGMENTED REALITY

2021 ◽  
Vol 58 (3) ◽  
pp. 137-142
Author(s):  
A.O. Dauitbayeva ◽  
◽  
A.A. Myrzamuratova ◽  
A.B. Bexeitova ◽  
◽  
...  
Keyword(s):  
Virtual Reality ◽  
Augmented Reality ◽  
User Interfaces ◽  
Three Dimensional ◽  
Spatial Arrangement ◽  
Linear Perspective ◽  
Three Dimensional Objects ◽  
Visualization Technology ◽  
Image Volume ◽  
Dimensional Object

This article is devoted to the issues of visualization and information processing, in particular, improving the visualization of three-dimensional objects using augmented reality and virtual reality technologies. The globalization of virtual reality has led to the introduction of a new term "augmented reality"into scientific circulation. If the current technologies of user interfaces are focused mainly on the interaction of a person and a computer, then augmented reality with the help of computer technologies offers improving the interface of a person and the real world around them. Computer graphics are perceived by the system in the synthesized image in connection with the reproduction of monocular observation conditions, increasing the image volume, spatial arrangement of objects in a linear perspective, obstructing one object to another, changing the nature of shadows and tones in the image field. The experience of observation is of great importance for the perception of volume and space, so that the user "completes" the volume structure of the observed representation. Thus, the visualization offered by augmented reality in a real environment familiar to the user contributes to a better perception of three-dimensional object.

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