scholarly journals Decision support systems configuration based on knowledge-driven automated service composition: requirements and conceptual model

2021 ◽  
Vol 186 ◽  
pp. 654-660
Author(s):  
Andrew Ponomarev ◽  
Nikolay Mustafin
Author(s):  
Alexander Smirnov ◽  
Tatiana Levashova ◽  
Andrew Ponomarev

Introduction: Due to the development of information and communication technologies and artificial intelligence, human-machine computing systems are becoming more widely used. However, in the vast majority of developments in this area, a human, in fact, plays the role of a “computing device”, who can only handle requests of a certain kind. Thus, human creativity and the ability to (self-)organize are largely discarded. Purpose: Developing a decision support concept based on the use of human-machine collective intelligence. Analyzing the current state of the problem in the field of constructing flexible human-machine systems. Proposing a conceptual model of the environment based on which decision support systems can be created. Results: A conceptual model of decision support is proposed based on human-machine collective intelligence. Its central concepts are: a) the problem at whose solution the human-machine collective activity is aimed, b) the collective of machines and people interacting through the environment to solve the problem, c) the process model which describes the decision support process in terms of information collection development and evaluation of alternatives. Practical relevance: The developed model can be a base to create a new class of decision support systems leveraging the self-organization potential of human-machine collectives.


2012 ◽  
Vol 4 (4) ◽  
pp. 24-38 ◽  
Author(s):  
Hayden Wimmer ◽  
Guisseppi Forgionne ◽  
Roy Rada ◽  
Victoria Yoon

This paper provides an integrated and comprehensive conceptual framework for knowledge based decision making support systems. Previous research has focused primarily on general decision support systems. The paper extends the previous work by presenting a framework to support specific decisions using knowledge marts that contain decision pertinent knowledge. A proposed methodology to test the effectiveness of this new model is proposed. The model presented provides much more specific knowledge support than previous systems.


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