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 HIERARCHICAL REPRESENTATION OF CAUSAL RELATIONSHIPS TO DETAIL EXPLANATIONS IN INTELLIGENT SYSTEMS

2021 ◽  
Vol 5 (4) ◽  
pp. 103-108
Author(s):  
Serhii Chalyi ◽  
Volodymyr Leshchynskyi
Keyword(s):  
Decision Making ◽  
Information System ◽  
Intelligent Systems ◽  
Input Data ◽  
Intelligent System ◽  
Decision Making Process ◽  
Causal Relationships ◽  
Hierarchical Representation ◽  
Cause And Effect ◽  
Intelligent Information

The subject of research in the article is the processes of constructing explanations in intelligent systems based on the use of causal dependencies. The aim is to develop a hierarchical representation of causal relationships between the actions of an intelligent system to form an explanation of the process of the system's operation with a given degree of generalization or detailing. Representation of the hierarchy of cause-and-effect relationships allows you to form an explanation at a given level of detail using the input data in the form of a temporally ordered sequence of events reflecting the known actions of an intelligent system. Tasks: structuring the hierarchy of cause-and-effect relationships for known variants of the decision-making process in an intelligent information system, considering the temporal ordering of the corresponding actions; development of a model of a multi-level representation of causal dependencies for description for explanations in an intelligent system. The approaches used are: counterfactual analysis of causality, used to describe alternative dependencies for possible decision-making options; linear temporal logic to reflect the temporal aspect of causation. The following results were obtained. A generalized hierarchy of cause-and-effect relationships is highlighted for the known variants of the process of obtaining recommendations in an intelligent information system based on the temporal ordering of the corresponding decision-making actions. A model of hierarchical representation of causal dependencies has been developed to describe explanations in an intellectual system with a given degree of detail. Conclusions. The scientific novelty of the results obtained is as follows. A model of hierarchical representation of time-ordered causal relationships is proposed to describe the explanations of the operation of an intelligent system with a given degree of detail. At the top level of the hierarchy, the model defines a generalized causal relationship between the event of using the input data and the event of the result of the system's operation. This connection describes the current task that the intelligent information system solves. At the lower level, cause-and-effect relationships are set between events sequential in time, between which there are no other events. At intermediate levels of the hierarchical representation, the causal dependencies of pairs of events are determined, between which there are other events. The developed model creates conditions for constructing explanations with a given degree of detailing of the actions of the decision-making process in an intelligent system. The model also provides the ability to describe early and late anticipation of alternative sequences of the decision-making process by describing causal dependencies for events between which there are other events.

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.

VARIOUS ASPECTS OF INTELLIGENT COLLABORATIVE EDUCATIONAL SYSTEMS

2017 ◽  
Vol 2 ◽  
pp. 67
Author(s):  
Sabina Katalnikova ◽  
Leonids Novickis
Keyword(s):  
Intelligent Systems ◽  
Intelligent System ◽  
System Development ◽  
Effective Means ◽  
Educational Systems ◽  
Knowledge Based ◽  
Knowledge Based Economy ◽  
Wide Range ◽  
Key Factor ◽  
System Knowledge

In connection with the transition to a knowledge-based economy, at a time when a key factor in the development of society is the accumulated human knowledge and skills, as well as the availability of a wide range of users, intelligent systems are becoming very popular. Accordingly, the demand of the ergonomic and effective means of designing this class system is growing as well. The most time-consuming and most important stage of intelligent system development is the formation of the system knowledge base which ultimately determines the efficiency and quality of the entire intelligent system. Knowledge representation and processing models and methods as well as the intelligent system development techniques operating on the basis of these methods and models have a crucial role in relation to this. The article explores the different aspects of intelligent collaborative educational systems, describes the overall structure of an intelligent collaborative educational system and reflects the different steps of development the system.

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 Self-Organization and Adaptation in Intelligent Systems

1998 ◽  
Vol 2 (6) ◽  
pp. 177-177
Author(s):  
Nikola Kasabov ◽  
◽  
Robert Kozma ◽  
Keyword(s):  
Neural Networks ◽  
Information Systems ◽  
Intelligent Systems ◽  
Adaptive Systems ◽  
Hebbian Learning ◽  
Intelligent System ◽  
Self Organizing Maps ◽  
Intelligent Information Systems ◽  
Intelligent Information ◽  
Self Organizing

This special issue is devoted to one of the important topics of current intelligent information systems-their ability to adapt to the environment they operate in, as adaptation is one of the most important features of intelligence. Several milestones in the literature on adaptive systems mark the development in this area. The Hebbian learning rule,1) self-organizing maps,2,3) and adaptive resonance theory4) have influenced the research in this area a great deal. Some current development suggests methods for building adaptive neurofuzzy systems,5) and adaptive self-organizing systems based on principles from biological brains.6) The papers in this issue are organized as follows: The first two papers present material on organization and adaptation in the human brain. The third paper, by Kasabov, presents a novel approach to building open structured adaptive systems for on-line adaptation called evolving connectionist systems. The fourth paper by Kawahara and Saito suggests a method for building virtually connected adaptive cell structures. Papers 5 and 6 discuss the use of genetic algorithms and evolutionary computation for optimizing and adapting the structure of an intelligent system. The last two papers suggest methods for adaptive learning of a sequence of data in a feed-forward neural network that has a fixed structure. References: 1) D.O. Hebb, "The Organization of Behavior," Jwiley, New York, (1949). 2) T. Kohonen, "Self-organisation and associative memory," Springer-Verlag, Berlin, (1988). 3) T. Kohonen, "Self-Organizing Maps, second edition," Springer Verlag, (1997). 4) G. Carpenter and S. Grossberg, "Pattern recognition by self-organizing neural networks," The MIT Press, Cambridge, Massachusetts, (1991). 5) N. Kasabov, "Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering," The MIT Press, CA, MA, (1996). 6) S. Amari and N. Kasabov "Brain-like Computing and Intelligent Information Systems," Springer Verlag, Singapore, (1997).

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”

scholarly journals Sebuah Kerangka Kerja untuk mengembangkan e-Tracer study berbasis Sistem Cerdas

2020 ◽  
Vol 4 (2) ◽  
pp. 82
Author(s):  
Rizaldi Akbar ◽  
Rahmi Hajriyanti
Keyword(s):  
Intelligent Systems ◽  
Intelligent System ◽  
System Development ◽  
Tracer Study ◽  
Tracer Studies ◽  
Web Based ◽  
Integration Framework ◽  
Tertiary Institutions ◽  
Development Method ◽  
Descriptive Research

Tracer studies are a method used by tertiary institutions to track, monitor, and supervise graduates that have been produced by tertiary institutions. These activities can make a performance measurement related to the implementation of education so far. Currently, the tracer study has been developed to become a system that can provide decisions and measure graduates using an intelligent agent (AI). In this study, the authors used a descriptive research method, namely developing an information system based on existing data and developed using PHP software, and the Dynamic System Development Method (DSDM) is a system development method. Researchers have implemented an AI integration framework in a web-based tracer study architecture. System evaluation is used as the next research iteration of the proposed decision support system, this study also aims to monitor graduates. With the existence of an intelligent system-based e-tracer study, it has become an evolution in studying artificial intelligence to support monitoring and as a tool in increasing graduates in Higher Education.Keywords:Systems Framework, Tracer study, Intelligent Systems, Web

scholarly journals Information system for monitoring of hypopituitarism in childhood and adolescence

2021 ◽  
Vol 2142 (1) ◽  
pp. 012017
Author(s):  
M O Rusakov ◽  
O S Krotova
Keyword(s):  
Information System ◽  
Programming Language ◽  
Medical Information ◽  
Information Storage ◽  
Medical Decision ◽  
Medical Information System ◽  
Childhood And Adolescence ◽  
Intellectual System ◽  
Intelligent Information ◽  
Personalized Approach

Abstract The article is devoted to the development of a medical information system (MIS) designed to monitor and predict hypopituitarism in children and teenagers. MIS combines the medical information storage and management system with an intellectual system of medical decision-making support. The information system is based on a database with information obtained from impersonal medical discharge papers of children and adolescents of the Altai Territory diagnosed with hypopituitarism. Medical discharge papers provide information about the patient’s health status while in the hospital, the results of examinations and treatment. Discharges form a complete medical history of the patient, and the information system allows assessing the disease in dynamics. The purpose of the intelligent information system is to predict the patient's growth in order to select the optimal treatment strategy. The intelligent decision support system is based on a machine-learning model trained on data from a database. The model was trained in the Python programming language. The MIS interface is developed using the C # programming language. The use of MIS in medical institutions will allow doctors to carry out a personalized approach to the monitoring and predicting the growth for patients, and to choose the optimal trajectory of treatment.

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