Metagraph Approach as a Data Model for Cognitive Architecture

Chernenkiy, Valeriy M.; Gapanyuk, Yuriy E.; Revunkov, Georgiy I.; Kaganov, Yuriy; Fedorenko, Yuriy S.

doi:10.1007/978-3-319-99316-4_7

Cited by 12 publications

(5 citation statements)

References 4 publications

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“…The method that works in real time has the following obvious advantages (Chernenkiy, V. et al, 2018):…”

Section: Resultsmentioning

confidence: 99%

“…There are a number of difficulties in the construction of anamorphosis, the overcoming of which is described in detail in (Terekhov et al, 2019;Chernenkiy, V. et al, 2018).…”

Section: The Methods Of Dynamic Metamorphosismentioning

confidence: 99%

“…In the course of research in (Terekhov et al, 2019), a variant of the modified anamorphic algorithm was proposed, in the calculated part of which an artificial neural network with one hidden layer was used, which allows to significantly reduce the operation time of the algorithm with an acceptable loss of computational accuracy. In (Chernenkiy, V. et al, 2018;Terekhov et al, 2019), the method of presentation of risks in the form of time series and their prediction using symbolic regression is presented, which is a method for constructing regression models by trying different random composition of functions from a given set. The implementation of symbolic regression involves the generation of a formula in the form of a syntactic tree, using the method of genetic programming (a special case of a genetic algorithm that works with syntactic trees of variable length).…”

Section: The Methods Of Dynamic Metamorphosismentioning

confidence: 99%

See 2 more Smart Citations

Cognitive Visualization in a Hybrid Intelligent Risk Controlling System

Skvortsova¹,

Terekhov²,

Chernenky³

et al. 2019

Proceedings of the 16th International Conference on Applied Computing 2019

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Currently developed hybrid intelligent systems that use a combination of traditional data processing methods and intelligent methods are able to analyze and assess the existing risks in all areas of human activity. This article shows the positive effect of cognitive computer visualization as a means of activating the mechanisms of visual-figurative thinking of the person making a decision in the tasks of controlling the integral indicator of risks. The essence of the anamorphic method is shown, which is the transition from one visual image built on the basis of Euclidean metrics to another visual image based on the quantitative metric of the selected risk. The mathematical formulation of the dynamic meta-anamorphine method is disassembled. The advantages of the constructed meta-anamorphosis in the risk controlling HIS are given. The problem of calculating the integral risk index, which combines the risks of different spheres of activity and visualizations built on this basis, is analyzed. Conclusions are made regarding the construction of a hybrid model based on the hierarchy analysis method and the dynamic meta-anamorphosis method. The directions of further research are determined.

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“…The method that works in real time has the following obvious advantages (Chernenkiy, V. et al, 2018):…”

Section: Resultsmentioning

confidence: 99%

“…There are a number of difficulties in the construction of anamorphosis, the overcoming of which is described in detail in (Terekhov et al, 2019;Chernenkiy, V. et al, 2018).…”

Section: The Methods Of Dynamic Metamorphosismentioning

confidence: 99%

Section: The Methods Of Dynamic Metamorphosismentioning

confidence: 99%

See 1 more Smart Citation

Cognitive Visualization in a Hybrid Intelligent Risk Controlling System

Skvortsova¹,

Terekhov²,

Chernenky³

et al. 2019

Proceedings of the 16th International Conference on Applied Computing 2019

View full text Add to dashboard Cite

show abstract

“…The genetic algorithm is then used to form a syntax tree which is the analytical description of the function. The fitness function of the algorithm is the error of approximation of the original time series of local indicators and the series generated by the syntax tree, and the genetic algorithm tries to minimise this error and achieve the maximum accuracy approximation accuracy [14][15].…”

Section: Anamorhing Algorithmmentioning

confidence: 99%

Visualization of Integrated Indicators of Information Risk in Decision Support Systems

Skvortsova

Terekhov

Proletarsky

et al. 2020

2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus)

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Decision-making is one of the essential functions of any manager. The means of visualization play a crucial role in supporting the manager in the decision-making process. This paper discusses the visualisation of information risks based on integral indicators. It provides a description of a mathematical model of the method of dynamic meta-anamorphosis and an example of its practical implementation. The advantages of using a hybrid approach to the development of dynamic metaanamorphosis are discussed.

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“…The subject of evolutionary databases and knowledge [8] is universal, but it is also supplemented by expert systems [9]; systems for understanding the natural Russian language [10]; decision-making systems for autonomous road vehicles [11]; systems for rapid summation of numbers [12]; automated control systems [13]. At the broader scientific level of modern technologies, research is being conducted in hybrid intelligent information systems [14], which study metagraphs [15]; cognitive architectures on them [16]; cognitive visualization [17]; neural networks [18]; news analysis systems architectures [19]; signal generation in computational intelligence [20]; semantic complex event processing [21]; disease prediction for medical institutions [22].…”

Section: Introductionmentioning

confidence: 99%

Logic Artificial Intelligence Application for the Students Individual Trajectories Introduction

Adamova

Varlamov

2020

ITM Web Conf.

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The individual trajectories and other student learning individualization forms introduction in engineering education are becoming an important competitive university advantage. However, you should be mindful of the choices of learning paths within the framework of requirements of Federal State Educational Standards (FSES): to receive a diploma the student must fulfill all requirements of the FSES. Individualization cannot be arbitrary and must fit within the established framework of the curriculum. Students can study more than the established requirements of the FSES on an individual program. On the other hand, within the established restrictions of the FSES, there are enough alternatives for individualized training to choose the specialization of a certain professional area. For example, for students studying information technology, this specialization can be a choice between different economy sectors: banks, telecommunications, industrial production, logistics, aircraft and rocket engineering, car industry, Internet companies, social networks, etc. If we take developing computer technologies as a basis, then individualization can consist in a more detailed study of one area in IT: databases; expert systems; data security; distributed registries; artificial intelligence (AI); machine learning and image recognition; understanding of natural language; automated systems management and technological processes; robotics, etc. As we can see, opportunities for individualization of training for students exist even within the strict framework of training standards. The paper provides examples of such individualization of training with BMSTU students. Practical work has shown that individualization complicates the work and increases the time spent by university staff on managing trajectories in student learning. The achievements of mivar technologies of logical artificial intelligence allow automating routine operations for managing students’ individual trajectories. In general, artificial intelligence can help in almost all tasks of engineering education in the transition to continuous people training “through life”.

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Metagraph Approach as a Data Model for Cognitive Architecture

Cited by 12 publications

References 4 publications

Cognitive Visualization in a Hybrid Intelligent Risk Controlling System

Cognitive Visualization in a Hybrid Intelligent Risk Controlling System

Visualization of Integrated Indicators of Information Risk in Decision Support Systems

Logic Artificial Intelligence Application for the Students Individual Trajectories Introduction

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