Applications of Decision-Support Systems in Sociotechnical Systems

2019 ◽  
pp. 188-213
Author(s):  
Tetiana Shmelova ◽  
Yuliya Sikirda
Keyword(s):  
Decision Support ◽  
Expert Systems ◽  
Decision Support Systems ◽  
Safety Management ◽  
Support Systems ◽  
Automated System ◽  
Civil Aviation ◽  
Air Traffic Controller ◽  
Forced Landing ◽  
External Management

In this chapter, the authors present conceptual models of decision support systems (DSS) and Expert systems (ES) for Human-Operator (H-O) of Air Navigation System (ANS), such as air traffic controller (ATC) in flight emergencies, Unmanned Aerial Vehicles (UAV) operator, Safety Management System (SMS), etc. The authors made an analysis of the International civil aviation organization (ICAO) documents on risk assessment and the impact of the social environment on the aviation system. Automated system of pre-flight information preparation with intelligent module for support the decision making (DM) about aircraft departure are presented and program realization of systems are shown. The authors obtained algorithm of determining the optimal aerodrome for the forced landing of aircraft is provided. Expert systems of aviation enterprise's estimation were developed. Inhomogeneous factors of internal and external management environment of aviation enterprise were generalized using set-theoretical approach. This gave possibilities to define that the level of safety of aviation activity has the greatest influence among factors of internal environment and global economic situation – among factors of external environment. The authors demonstrate some interesting applications of DM in Socio-Technical Systems (STS). The DSS for H-O of ANS in the emergency situational were developed. Examples of DSS: DSS of ATC in the emergency situational; the flight dispatcher's DSS; DSS of UAV's operator, etc. In addition, the chapter presents some cases of DSS developed by the authors and colleagues at National Aviation University, Ukraine.

Applications of Decision-Support Systems in Sociotechnical Systems

2017 ◽  
pp. 182-214
Author(s):  
Tetiana Shmelova ◽  
Yuliya Sikirda
Keyword(s):  
Decision Support ◽  
Expert Systems ◽  
Decision Support Systems ◽  
Safety Management ◽  
Support Systems ◽  
Automated System ◽  
Civil Aviation ◽  
Air Traffic Controller ◽  
Forced Landing ◽  
External Management

In this chapter, the authors present conceptual models of decision support systems (DSS) and Expert systems (ES) for Human-Operator (H-O) of Air Navigation System (ANS), such as air traffic controller (ATC) in flight emergencies, Unmanned Aerial Vehicles (UAV) operator, Safety Management System (SMS), etc. The authors made an analysis of the International civil aviation organization (ICAO) documents on risk assessment and the impact of the social environment on the aviation system. Automated system of pre-flight information preparation with intelligent module for support the decision making (DM) about aircraft departure are presented and program realization of systems are shown. The authors obtained algorithm of determining the optimal aerodrome for the forced landing of aircraft is provided. Expert systems of aviation enterprise's estimation were developed. Inhomogeneous factors of internal and external management environment of aviation enterprise were generalized using set-theoretical approach. This gave possibilities to define that the level of safety of aviation activity has the greatest influence among factors of internal environment and global economic situation – among factors of external environment. The authors demonstrate some interesting applications of DM in Socio-Technical Systems (STS). The DSS for H-O of ANS in the emergency situational were developed. Examples of DSS: DSS of ATC in the emergency situational; the flight dispatcher's DSS; DSS of UAV's operator, etc. In addition, the chapter presents some cases of DSS developed by the authors and colleagues at National Aviation University, Ukraine.

scholarly journals TOOL FOR EFFECTIVE EXPERT INTEROPERABILITY IN E-GOVERNMENT DECISION SUPPORT SYSTEMS

2021 ◽  
Vol 2 ◽  
pp. 38-50
Author(s):  
Sergey Sapegin ◽  
◽  
Gennady Riabtsev ◽  
Elena Zubareva ◽  
Igor Basantsov ◽  
...  
Keyword(s):  
Decision Support ◽  
Expert Systems ◽  
Decision Support Systems ◽  
Resource Constraints ◽  
Support Systems ◽  
Software Tool ◽  
Automated System ◽  
Systems Architecture ◽  
Government Decision ◽  
Low Efficiency

The article solves the problem of low efficiency of traditional methods for organizing the expert interaction in e-government decision support systems. It is shown that the activity of a highly qualified expert is still insufficiently organized, systematized and informationally provided, although expert assessments significantly affect the effectiveness, efficiency and economy of decisions. The requirements for automated expert systems are formulated. The features of the interaction of experts with each other and with the decision maker are taken into account. The place of automated expert systems in e-government decision support systems has been identified. It is shown that qualified users (experts who are able to evaluate (transform) the data, to generate variants of decision, to establish the rules for choosing the best) are an integral "element" of decision support systems architecture. At the same time, automated expert systems are mandatory component of decision support systems. An expert interoperability software tool, the PsycheaEXPERTUS automated system, is developed and implemented. Testing of this system confirmed that the use of such systems in e-government will simplify expert procedures, reduce unproductive time spent on organizing and conducting face-to-face consultations, and increase the efficiency of attracting specialists to make public policy, when the uncertainty of the environment and resource constraints exist. Further research is planned to be aimed at overcoming the psychological unpreparedness of decision makers for the widespread use of expert systems in Ukraine.

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.

Integration of Automation and Clinical Decision Support Systems

2016 ◽  
pp. 165-185 ◽  
Author(s):  
Ronnel Nallas ◽  
Jane Moon
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Chemistry ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Automated System ◽  
Laboratory Staff ◽  
Major Teaching

As the world of pathology and laboratory medicine has increasingly headed in the direction of automation, implementation of Clinical Decision Support Systems are becoming a vital part of the process. The advances in technology and costs of human resources are factors pushing for automation. This chapter addresses the advantages and issues encountered during the installation of the new automated system Roche Cobas 8000 and Middleware IT3000 for the Clinical Chemistry Laboratory of one of the major teaching hospitals in Australia. The input and cooperation of Laboratory staff, Clinicians, Roche Diagnostics and LEAN processes has resulted in a fine example of how automation and clinical decision support systems play a major role in Improving Health Management.

Decision support systems for silviculture planning in Canada

1995 ◽  
Vol 71 (3) ◽  
pp. 291-298 ◽  
Author(s):  
William A. Thompson ◽  
Gordon F. Weetman
Keyword(s):  
Decision Support ◽  
Expert Systems ◽  
Decision Support Systems ◽  
Key Words ◽  
Support Systems ◽  
Technical Information ◽  
Telephone Interviews ◽  
Further Development

An attempt is made to identify presently available Decision Support Systems (DSS) for silviculture planning and operations. Recent sources of technical information are also identified. Based on telephone interviews, the use of and needs for silviculture DSS are outlined for eight of Canada's Model Forests. Recommendations are made for further development of DSS for Canadian silviculture. Key words: decision support systems, expert systems, silvicultural planning

Integrating Expert Systems and Decision Support Systems

MIS Quarterly ◽  
1986 ◽  
Vol 10 (2) ◽  
pp. 121 ◽  
Author(s):  
Efraim Turban ◽  
Paul R. Watkins
Keyword(s):  
Decision Support ◽  
Expert Systems ◽  
Decision Support Systems ◽  
Support Systems
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