scholarly journals DEVELOPMENT OF A METHOD FOR THE INTERACTIVE CONSTRUCTION OF EXPLANATIONS IN INTELLIGENT INFORMATION SYSTEMS BASED ON THE PROBABILISTIC APPROACH

2021 ◽  
pp. 39-45
Author(s):  
Serhii Chalyi ◽  
Volodymyr Leshchynskyi
Keyword(s):  
Intelligent Systems ◽  
System Analysis ◽  
Intelligent System ◽  
Probabilistic Approach ◽  
User Interaction ◽  
Black Box ◽  
Interaction Pattern ◽  
Interactive Method ◽  
Analysis Methods ◽  
Intelligent Information

Subject: the use of the apparatus of temporal logic and probabilistic approaches to construct an explanation of the results of the work of an intelligent system in order to increase the efficiency of using the solutions and recommendations obtained. Purpose: development of a method for constructing explanations in intelligent systems with the ability to form and evaluate several alternative interpretations of the results of the operation of such a system. Tasks: justification for the use of the black box principle for interactive construction of explanations; development of a pattern explanation model that provides for probabilistic estimation; development of a method of interactive construction of explanations on the basis of the probabilistic approach. Methods: methods of data analysis, methods of system analysis, methods of constructing explanations, models of knowledge representation. Results: A model of the explanation pattern is proposed, which contains temporal regulations reflecting the sequence of user interaction with an intelligent system, which allows the formation of explanations based on a comparison of the actions of the current user and other well-known users. An interactive method for constructing explanations based on a probabilistic approach has been developed; the method uses patterns of user interaction with an intelligent system and contains phases of constructing patterns of explanations and forming explanations using the obtained patterns. The method organizes the received explanations according to the likelihood of use, which makes it possible to form target and alternative explanations for the user. Conclusions: The use of the black box principle for the development of a probabilistic approach to the construction of explanations in intelligent systems has been substantiated. A model of a pattern of explanations based on temporal regulations is proposed. The model reflects the sequence of user interaction with the intelligent system when receiving decisions and recommendations and contains an interaction pattern as part of temporal regulations that have weight, and also determines the likelihood of using the user interaction pattern. An interactive method for constructing explanations has been developed, considering the interaction of the user with the intelligent system. The method includes phases and stages of the formation of regulations and patterns of user interaction with the determination of the probability of their implementation, as well as the ordering of patterns according to the probability of their implementation. The implementation of the method was carried out when constructing explanations for recommender systems.

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 Assessing the quality of intelligent systems with flexible programmable architecture

Ergodesign ◽  
2021 ◽  
Vol 2021 (1) ◽  
pp. 36-40
Author(s):  
Nataliya Sukhanova
Keyword(s):  
Intelligent Systems ◽  
Research Method ◽  
System Analysis ◽  
Intelligent System ◽  
Assessment System ◽  
System Quality ◽  
System Reconfiguration ◽  
Programmable Architecture ◽  
Flexible Architecture

The purpose of this work is to assess the quality of functioning intelligent systems. Tasks to be solved: synthesis of an intelligent system based on unified modules, system quality assessment, system reconfiguration at a quality decrease. The research method is system analysis. A new flexible programmable architecture of intelligent systems has been developed. The flexible architecture of an intelligent system allows you to change the mutual relationships between subsystems, components and modules. The intelligent system is implemented on the basis of the unified modules that contain programmable switches. Switches are connected to the system inputs and outputs and are networked to transmit information.

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 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.

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 and Prognostics of Energy Conversion Processes via Knowledge-Based Systems

Proceedings ◽  
2020 ◽  
Vol 58 (1) ◽  
pp. 1
Author(s):  
Roberto Melli ◽  
Enrico Sciubba
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
Real Time ◽  
System Analysis ◽  
Intelligent System ◽  
Time Derivative ◽  
Time History ◽  
Control Procedure ◽  
Time Data ◽  
Ongoing Research

This paper presents a critical and analytical description of an ongoing research program aimed at the implementation of an expert system capable of monitoring, through an Intelligent Health Control procedure, the instantaneous performance of a cogeneration plant. The expert system is implemented in the CLIPS environment and is denominated PROMISA as the acronym for Prognostic Module for Intelligent System Analysis. It generates, in real time and in a form directly useful to the plant manager, information on the existence and severity of faults, forecasts on the future time history of both detected and likely faults, and suggestions on how to control the problem. The expert procedure, working where and if necessary with the support of a process simulator, derives from the available real-time data a list of selected performance indicators for each plant component. For a set of faults, pre-defined with the help of the plant operator (Domain Expert), proper rules are defined in order to establish whether the component is working correctly; in several instances, since one single failure (symptom) can originate from more than one fault (cause), complex sets of rules expressing the combination of multiple indices have been introduced in the knowledge base as well. Creeping faults are detected by analyzing the trend of the variation of an indicator over a pre-assigned interval of time. Whenever the value of this ‘‘discrete time derivative’’ becomes ‘‘high’’ with respect to a specified limit value, a ‘‘latent creeping fault’’ condition is prognosticated. The expert system architecture is based on an object-oriented paradigm. The knowledge base (facts and rules) is clustered—the chunks of knowledge pertain to individual components. A graphic user interface (GUI) allows the user to interrogate PROMISA about its rules, procedures, classes and objects, and about its inference path. The paper also presents the results of some simulation tests.

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.

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.

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