Multi-Objective Based Multi-Agent Decision-Making System
Închide
Articolul precedent
Articolul urmator
109 0
SM ISO690:2012
MELNIC, Radu, ABABII, Victor, SUDACEVSCHI, Viorica, SACHENKO, Oleg, BOROZAN, Olesea, LENDIUK, Taras. Multi-Objective Based Multi-Agent Decision-Making System. In: IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications: IDAACS 2023, Ed. 12, 7-9 septembrie 2023, Dortmund. New Jersey: Institute of Electrical and Electronics Engineers Inc., 2023, Ediția 12, pp. 834-839. ISBN 979-835035805-6. DOI: https://doi.org/10.1109/IDAACS58523.2023.10348725
EXPORT metadate:
Google Scholar
Crossref
CERIF

DataCite
Dublin Core
IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications
Ediția 12, 2023
Conferința "IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications"
12, Dortmund, Germania, 7-9 septembrie 2023

Multi-Objective Based Multi-Agent Decision-Making System

DOI:https://doi.org/10.1109/IDAACS58523.2023.10348725

Pag. 834-839

Melnic Radu1, Ababii Victor1, Sudacevschi Viorica1, Sachenko Oleg2, Borozan Olesea1, Lendiuk Taras2
 
1 Technical University of Moldova,
2 West Ukrainian National University, Ternopil
 
 
Disponibil în IBN: 26 februarie 2024


Rezumat

Multi-objective decision systems for the management, monitoring and control of complex processes require the application of new methods and models of abstraction and formal description based on intelligent computing structures to ensure optimal overall decision-making. These computing structures can be built on the basis of Multi-Agent systems. In this paper it is proposed the development of a Multi-Objective Based Multi-Agent Decision-Making System that ensures the process of searching for the optimal solution based on genetic algorithms and its application in the decision-making process. The Multi-Agent system features a distributed computing structure consisting of lots of heterogeneous data processing nodes. The functionality of agents is described based on mathematical models and sequence diagram, which explains the interaction between the set of Agents. The structure of the decision-making system is presented on two levels of abstraction: the Multi-Agent level of production and management, and the Information level of communication, storage and data processing. 

Cuvinte-cheie
decision-making, mathematical models, Multi-Agent system, Multi-Objective Optimization, Sequence Diagram, Smart Agriculture