Research On Key Techniques Of Multi-agent Platform For Distributed Simulation Of Complex System

With the arrival of the 21st century, problems about complex system and complexity science become increasingly noticeable. These problems involve the environment, resources, economy, population, health, disaster, peace, security and etc, which are concerned with the human survival and the sustainable development of the society. To solve these problems, people must rely on the interdisciplinary collaboration and integration and hold a wholeness perspective.Under the guide of the CAS theory, multi-agent modeling and simulation provides a new approach to study complex systems. It becomes one of the most popular methods in this domain. To satisfy the requirements of large scale complex system simulation, this thesis studies the architecture and key techniques of multi-agent platform for distributed simulation of complex systems.Firstly, according to the requirements of the CAS theory, the methodology of multi-agent complex system distributed simulation is described, and the way to implement multi-agent complex system distributed simulation is formalized. The base techniques of multi-agent platform for distributed simulation of complex systems are summarized in four aspects: techniques providing distributed computation support, techniques providing simulation support, techniques providing multi-agent modeling support and methods for observation and intervention of the simulation. The architecture of a multi-agent platform for complex system distributed simulation is then proposed.Secondly, this thesis studies the issues about the distributed modeling and partitioning of the environment. Several vague concepts about the environment are distinguished. After analyzed the important impacts of environment on multi-agent complex system simulation, the necessity of distributed simulation of the environment is presented. Then a distributed environment model is introduced. In this model, the whole environmental space is divided into grids and each grid is modeled separately. The method to determine the size of the grid is also presented. The issue to partition the environment is described in the form of partitioning the environment-model-load graph. This problem is proved to be NP-hard, and there is no polynomial time approximation algorithm with finite approximation factor unless P=NP. Based on the heuristic information, a pseudo greedy bisection partitioning algorithm is proposed. It can be used to bisection the environment approximately. To solve the problem of partitioning the environment generally, a k-way recursive pseudo bisection partitioning algorithm is given based on the divide-and-conquered technique. Experimental results indicate that these two algorithms can produce satisfactory partitions with relatively smaller cost of time than current methods.Thirdly, a hierarchical causal order time management algorithm is presented. After analyzing the necessity of time management in distributed simulation and introducing the special demands of multi-agent distributed simulation for complex systems, drawbacks of timestamp order time management algorithms are pointed out. Then the research and development of current causal order time management algorithms are surveyed and an improved algorithm based on effective immediate causal predecessor is proposed according to a causal order time management algorithm named MSES. The improved algorithm is extended hierarchically to fulfill the demands of large scale distributed simulations. Experimental results show the correctness and efficiency of our algorithm.Fourthly, a multi-agent platform for distributed simulation of complex system named Advanced JCass is implemented and the key issues in the implementation are conquered.Finally, to validate our works, a simulation application to forecast the trend of public scientific literacy and study the effect of science popularization in Hunan province is designed and implemented. This problem is considered as a typical complexity problem, and weaknesses of current methods are pointed out. Then an approach to model the wholeness of complex system is proposed, which considers both time-space structure and external events. The process to modeling and simulating the wholeness is also presented. With this approach, the wholeness of science popularization system in Hunan province is modeled, and several models for environment, agents and policy are explicitly constructed. Based on the analysis of the impact of external events on the wholeness of the system, methods to model external events are given. Initialized and executed with data from real system, execution results show that our platform can simulate complex systems correctly.