Комплекс физико-математического моделирования при функционировании мультиагентных систем

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.

Evolutionary Games in the Agricultural Product Quality and Safety Information System: A Multiagent Simulation Approach

This paper aims at identifying the key factors to maintain the quality and safety of agricultural products in the agricultural product quality and safety information system (APQSIS). Based on the theoretical framework of information entropy and complexity, this paper uses the dynamic evolutionary game model and the multiagent modeling and simulation to discuss the APQSIS agents’ equilibrium strategies and the effects of their interactive behaviors on the APQSIS evolutionary stability with asymmetric information. The results show that the governmental supervision and intermediary organizations are significant to assuring agricultural product quality and safety (APQS) as well as the effective transmission of APQS information in stable environments with low complexity.

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

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.

Multiagent modeling of sequential multiunit japanese auctions

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.

Agent-Based Modeling and Simulation for the Bus-Corridor Problem in a Many-to-One Mass Transit System

With the growing problem of urban traffic congestion, departure time choice is becoming a more important factor to commuters. By using multiagent modeling and the Bush-Mosteller reinforcement learning model, we simulated the day-to-day evolution of commuters’ departure time choice on a many-to-one mass transit system during the morning peak period. To start with, we verified the model by comparison with traditional analytical methods. Then the formation process of departure time equilibrium is investigated additionally. Seeing the validity of the model, some initial assumptions were relaxed and two groups of experiments were carried out considering commuters’ heterogeneity and memory limitations. The results showed that heterogeneous commuters’ departure time distribution is broader and has a lower peak at equilibrium and different people behave in different pattern. When each commuter has a limited memory, some fluctuations exist in the evolutionary dynamics of the system, and hence an ideal equilibrium can hardly be reached. This research is helpful in acquiring a better understanding of commuter’s departure time choice and commuting equilibrium of the peak period; the approach also provides an effective way to explore the formation and evolution of complicated traffic phenomena.