scholarly journals Prognostics of a multistack PEMFC system with multiagent modeling

2018 ◽  
Vol 7 (1) ◽  
pp. 76-87 ◽  
Author(s):  
Jie Liu ◽  
Enrico Zio
Keyword(s):  
Multiagent Modeling

scholarly journals A Multiagent Modeling and Investigation of Smart Homes With Power Generation, Storage, and Trading Features

2013 ◽  
Vol 4 (2) ◽  
pp. 659-668 ◽  
Author(s):  
Salman Kahrobaee ◽  
Rasheed A. Rajabzadeh ◽  
Leen-Kiat Soh ◽  
Sohrab Asgarpoor
Keyword(s):  
Power Generation ◽  
Smart Homes ◽  
Multiagent Modeling

scholarly journals Multiagent modeling of sequential multiunit japanese auctions

2016 ◽  
pp. 129-137
Author(s):  
A.L. Yalovets ◽  
Keyword(s):  
Point Of View ◽  
Suggested Method ◽  
Dominant Strategies ◽  
Optimal Auction ◽  
Multiagent Modeling

Features of modeling of auctions are investigated from the point of view of simulation (multiagent) modeling. The characteristic of auctions as object of modeling is resulted. Statement of a task carries out and the method of construction of the mechanism of carrying out of sequential multiunit japanese auctions, which provides use by agents of dominant strategies is offered and allows to construct optimal auction. Efficiency of the suggested method experimentally proves to be true.

Intelligent multiagent modeling and decision system for battlefield

1999 ◽  
Author(s):  
Norman Coleman ◽  
Ching-Fang Lin ◽  
Jianhua Ge ◽  
Sarah Braasch
Keyword(s):  
Decision System ◽  
Multiagent Modeling

Комплекс физико-математического моделирования при функционировании мультиагентных систем

2020 ◽  
Author(s):  
YU.I. Nechaev
Keyword(s):  
Mathematical Modeling ◽  
Intelligent Agents ◽  
Intelligent Systems ◽  
High Performance ◽  
Evolutionary Dynamics ◽  
Formal System ◽  
Multi Agent Systems ◽  
Multiagent Modeling ◽  
Physical And Mathematical Modeling ◽  
Urgent Computing

Рассматривается повышение эффективности функционирования мультиагентных систем при использовании программного комплекса физико-математического моделирования (ФММ). Функциональные элементы комплекса обеспечивают контроль экстремальных ситуаций на основе динамической модели современной теории катастроф (СТК), интегрирующей интеллектуальных технологии и высокопроизводительные вычисления. Особенности построения комплекса связаны с развитием новых подходов к физико-математическому моделированию динамики сложных систем в эволюционирующей среде. Вычислительная среда эволюционной динамики представлена как активная динамическая система (АДС) на основе совокупности взаимодействующих интеллектуальных агентов (ИА) в среде мультиагентного моделирования (Multiagent Modeling System MMS), обеспечивающей информационные и управляющие связи, реализующие модель коллективного интеллекта при взаимодействии ИА в режиме экстренных вычислений (Urgent Computing UC) 1 5. Модели контроля экстремальных ситуаций разрабатываются в рамках логического базиса нечеткой формальной системы (НФС). Приведены примеры реализации разработанной стратегии в бортовых интеллектуальных системах новых поколений.An increase in the efficiency of multi-agent systems is considered when using the software package for physical and mathematical modeling (FMM). The functional elements of the complex provide control of extreme situations on the basis of a dynamic model of modern catastrophe theory (MCT), integrating intelligent technologies and high-performance computing. Features of the complex construction are associated with the development of new approaches to the physical and mathematical modeling of the dynamics of complex systems in an evolving environment. The computing environment of evolutionary dynamics is presented as an active dynamic system (ADS) based on a set of interacting intelligent agents (IA) in a Multiagent Modeling System (MMS), which provides information and control communications that implement the collective intelligence model in the interaction of IA in urgent computing mode (Urgent Computing - UC). Models for controlling extreme situations are developed within the framework of the logical basis of the fuzzy formal system (FFS). Examples of the implementation of the developed strategy in the onboard intelligent systems of new generations are given.

Multiagent Modeling and Simulation of Hydraulic Management of the Camargue

SIMULATION ◽  
2005 ◽  
Vol 81 (3) ◽  
pp. 201-221 ◽  
Author(s):  
Bernard Espinasse ◽  
Nathalie Franchesquin
Keyword(s):  
Modeling And Simulation ◽  
Multiagent Modeling

scholarly journals An Evolutionary Game Model of Knowledge Workers’ Counterproductive Work Behaviors Based on Preferences

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Si-hua Chen
Keyword(s):  
Knowledge Workers ◽  
Evolutionary Game ◽  
Working Environment ◽  
Counterproductive Work Behaviors ◽  
Game Model ◽  
Work Behaviors ◽  
Key Factors ◽  
Great Loss ◽  
Modeling Simulation ◽  
Multiagent Modeling

Knowledge workers’ counterproductive work behaviors (CWB) always cause great loss to enterprises, but it is hard to supervise these behaviors. Based on the analysis of the causes of these behaviors, this paper builds a theoretical model of knowledge workers’ CWB and proposes that knowledge workers’ CWB are influenced by both rational and irrational factors. Regarding contextual factors and individual factors as risk preferences of knowledge workers, this paper establishes an asymmetrical evolutionary game model of enterprise supervision. Then, multiagent modeling simulation is conducted to discuss the effect of both formal and informal constraints on knowledge workers’ CWB and, based on it, the intervention strategies of enterprises are proposed. The simulation results show that the effect of informal constraints is bigger than the effect of formal constraints. The working environment and knowledge workers’ personality traits are the key factors to produce CWB.

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