Research On The Modeling And Simulation Of Multi-agent System With Complex Event Scheduling Logic And Bayesian Network Decision-making

In recent years, more and more researchers in the field of management science have paidattention to agent-based modeling and simulation(ABMS) method and selected ABMS method astheir important research tool. But there are still many imperfections in the research of ABMS method——the lack of simple and effective modeling methodology, and some important technical issues tobe resolved, these defects have restricted the further development and applications of ABMS method.For this, the thesis concentrates on the modeling and simulation study of multi-agent system (MAS)with complex event scheduling logic and Bayesian network decision-making in order to make somevaluable works to improve the ABMS method, that involve designing simulation event schedulingapproach for MAS, researching the decision-making model of agent under uncertainty, anddeveloping an management experts-oriented ABMS modeling methodology. The research results haveimportant theoretical and practical significance to enrich the ABMS theory, and improve the level ofABMS application on complex management problems.The simulation process control method (named in the thesis as5-step scanning method) forMAS with complex event scheduling logic is proposed which combines the principle of discrete eventsimulation theory with the characteristics of MAS. Based on the strategy of separating simulationscheduling logic, agent behavior logic, and agent interaction logic, a reasonable classification ofmulti-agent simulation events is given,the framework and the process control algorithm for MASsimulation are designed, and the software developing of the method is achieved. The method caneffectively resolve the difficulty of dealing with simulation scheduling logic in ABMS application.Bayesian networks and influence diagrams are introduced in ABMS method, and the multi-agentBayesian network decision-making model under uncertainty is developed. On the basis of analysis ofthe MAS decision type, solutions for the independent decision-making and collaborativedecision-making of the agent are established, and repetitive decision-making Bayesian online learningalgorithm is designed according to the characteristics of the MAS. Based on the principle ofeasy-to-use, universal, comprehensive, and scalable, the software developing of above models areimplemented, the SmartAgent software toolkit is developed to improve the ease of use, in which the5-step scanning method and multi-agent Bayesian network decision-making model are packaged.By reference of software engineering theories, a UML based management experts-orientedABMS modeling methodology——MEOLW is proposed. Through "problem domain model-logical model-simulation model " these three basic stages, the exchanges and matches between managementexperts and IT experts are accomplished in MEOLW methodology, MEOLW provides a way fordifferent type experts to collaboratively develop more complex multi-agent simulation system.Finally, in the case studies, a multi-agent system model of China’s real estate market ecological isdeveloped and the simulation experiments are made. The trend of China’s real estate market isaccurately reproduced in the simulation experiments, that proves the effectiveness of the techniquesand methods developed in the thesis. The model proposed in the thesis also provide some referencefor the study of China’s real estate market.