18 examples of mivar expert systems

2021 ◽  
Author(s):  
Oleg Varlamov
Keyword(s):  
Artificial Intelligence ◽  
Expert Systems ◽  
Control Systems ◽  
Knowledge Bases ◽  
Automated Control ◽  
Postgraduate Students ◽  
Subject Areas ◽  
Applied Artificial Intelligence ◽  
New Generation ◽  
Artificial Intelligence Systems

Many years of research on mivar technologies of logical artificial intelligence have allowed us to create a new powerful, versatile and fast tool, which is called "multidimensional open gnoseological active net" — "multidimensional open gnoseological active net: MOGAN". This tool allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format, and it can be used to model cause-and-effect relationships in different subject areas and create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with "Big Knowledge". The reader, after studying this tutorial, you will be able to create mivar expert system with the help of CASMI Wi!Mi. Designed for students, bachelors, masters and postgraduate students studying artificial intelligence methods, as well as for users, experts and specialists, creating a system of information processing and management, mivar models, expert systems, automated control systems, systems of decision support and Recommender systems.

Fundamentals of creating MIVAR expert systems

2021 ◽  
Author(s):  
Oleg Varlamov
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Expert System ◽  
Expert Systems ◽  
Decision Support Systems ◽  
Intelligent Systems ◽  
Autonomous Robots ◽  
Support Systems ◽  
Knowledge Bases ◽  
Computer Engineering

Methodological and applied issues of the basics of creating knowledge bases and expert systems of logical artificial intelligence are considered. The software package "MIV Expert Systems Designer" (KESMI) Wi!Mi RAZUMATOR" (version 2.1), which is a convenient tool for the development of intelligent information systems. Examples of creating mivar expert systems and several laboratory works are given. The reader, having studied this tutorial, will be able to independently create expert systems based on KESMI. The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

Mivar databases and rules

2021 ◽  
Author(s):  
Oleg Varlamov
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Expert System ◽  
Expert Systems ◽  
Decision Support Systems ◽  
Support Systems ◽  
Knowledge Bases ◽  
Data Models ◽  
Active Network ◽  
New Model

The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge". The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

INTELLECTUALIZATION OF AGROTECHNOLOGIES MANAGEMEN

1970 ◽  
pp. 24-28
Author(s):  
I. М. Mikhaylenko
Keyword(s):  
Decision Making ◽  
Control Systems ◽  
Training Sample ◽  
Knowledge Bases ◽  
Automated Control ◽  
Agricultural Technologies ◽  
Expert Control ◽  
Agricultural Industry ◽  
Rural Producers ◽  
High Level

The agricultural industry is one of the most important areas of digitalization of the economy. At the same time, the content basis of digitalization is the technology of precision farming (TZ), which implements the tasks of agrotechnology management. These tasks are divided into two main groups according to the type of executive control system. The first group includes organizational management tasks, embodied in control decision-making systems (DSS) and implemented by management at various levels. The second group includes the tasks of managing field agricultural technologies, embodied in automated control systems (ASUAT). These tasks are implemented by automated and robotic technological machines. The effectiveness of management systems depends on the degree of human participation in the management process, i.e. on the level of his intellectualization. The high level of intellectualization depends on how widely the achievements of modern management science are involved in the creation of control systems. Such achievements are most fully used in analytical systems DSS and ASUAT. However, their actual use is faced with the lack of the required qualifications of rural producers. This problem can be solved by moving to expert control systems that do not require complex multi-step calculations. At the same time, the breakthrough level of such systems can be provided by cloud-based information systems, when knowledge bases (BRs) in expert systems will be formed in information processing centers and transmitted through the public cloud to local DSS and MISS. In order to make optimal decisions on KBs in local DSS and ASUAT, pattern recognition algorithms or special decision-making models can be used, the parameters of which are estimated by the KB, considered as a training sample.

scholarly journals Development features and the role of glossaries in the creation of military IT-projects

2020 ◽  
Vol 4 ◽  
pp. 62-73
Author(s):  
V.F. Hrechaninov ◽  
◽  
T.K. Yeremenko ◽  
Yu.G. Pylypenko ◽  
◽  
...  
Keyword(s):  
Control Systems ◽  
Knowledge Bases ◽  
Subject Area ◽  
It Projects ◽  
Automated Control ◽  
It Systems ◽  
Corporate System ◽  
The Creation ◽  
The Military

The paper examines the role and features of the development of glossaries in the creation of information and control systems in the field of military affairs. The interest in this topic is due to the growing vol-ume of information that must be taken into account when creating such systems. A huge number of standards in this area offer a lot of definitions of terms that can contradict each other. On the other hand, the role of glossaries in projects related to the development of information systems in general is becoming more significant. Terminology management is necessary in the interaction of various groups of developers, for interaction between developers and users, and is also the basis for conceptual infor-mation modeling of automated control systems. The peculiarities of developing IT-projects require a deep study of the subject area, which is related to the study of the appropriate terminology, both from foreign and national sources. Ukraine's course to join European military structures and NATO requires the study of relevant standards and other available documentation, including Multilateral Interoperabil-ity Program (MIP) documents. A large number of terms are related to the application of world standards of the development of IT systems. Management in the military field is hierarchical, as in any state sys-tem. Such a corporate system has the ability to create a methodological center that includes unified knowledge bases on information technology and subject area standards, terminology programs. Creation of a glossary for the implementation of IT-project that would satisfy the requirements of both users and developers is a laborious task that requires processing of a large amount of information. The paper dis-cusses ways of developing thematic glossaries based on standardization and terminology.

scholarly journals Artificial intelligence systems in the mechanism of criminal procedure evidence

2020 ◽  
pp. 89-95
Author(s):  
Aleksey Afanas'ev
Keyword(s):  
Artificial Intelligence ◽  
Expert Systems ◽  
Criminal Procedure ◽  
Artificial Intelligence Systems

The article consistently justifies the potential of artificial intelligence systems in the study of the mechanism of criminal procedure evidence and its implementation in practice. The author draws up the hypothesis of «individuality of the mechanism of criminal procedure proof» and builds up a method of application of expert systems for its verification.

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