【作者】 曹琦；

【导师】 何中市；

【作者基本信息】 重庆大学 ， 计算机软件与理论， 2010， 博士

【摘要】 复杂自适应系统通常由许多并行的适应性主体组成,具有多层次的结构,具有智能性和自适应性,并呈现“涌现”的特征,难以对其进行形式化的解析与验证。仿真是研究复杂自适应系统的一个重要途径,通过对系统进行形式化描述,建立分布式协同仿真模型,对于领域专家研究系统行为和特性、预测系统变化范围和发展趋势,领域人员从事系统内的作业活动、进行业务训练和演习等,都具有十分重要的意义。目前,国际上基于复杂自适应系统理论的仿真建模,均以Agent/Swarm为基本单元,对于小规模的系统建模方便,仿真速度较快。但是,随着系统规模的增大,大量主体间的交互问题对仿真提出了更高的要求,需要进一步实现分布交互式仿真;基于事件队列调度的Swarm不能充分仿真主体间事件的随机性,需要采用更为有效的方式实现仿真对象之间的协同性;传统数学模型不能很好地描述系统推进过程中表现出的复杂态势变化与内在的动力学机制,需要进一步刻画系统的动态性;Swarm中更关注的是主体个体的自主性能,对各群体的中央控制及其相互间的横向联系并没有进行深入描述,需要在层次化、模块化方面进行加强。针对上述问题,本文在系统分析复杂自适应系统基础理论的基础上,提出了联合仿真建模技术框架;将Agent建模思想引入到具有层次化、模块化优势的DEVS建模规范中,将其扩展为Agent-DEVS形式化描述规范;在HLA体系结构下,实现了Agent-DEVS模型的分布交互式协同仿真;基于SVM学习方法,完成了Agent-DEVS模型的参数优化;最后构建了抢险救灾物资保障模拟训练原型系统。归纳起来,本文主要创新性成果包括:①提出了一种能够对智能性和协作性进行描述的离散事件系统规范Agent-DEVS。该规范以并行DEVS为基础,状态元组被扩充成为Agent特征元组;增加了体现个体智能性的Agent模型元组;并将模型端口的输入、输出扩展成为体现社会协作性的Agent消息类型。分析了其原子模型与耦合模型的形式化描述,证明了Agent-DEVS模型的耦合封闭性,并给出了模型实现算法。仿真试验结果表明:1)Agent-DEVS规范能够直接描述智能行为。其优势主要体现在建模性能上,通过将各类参数转化为知识库中的变量,并建立相应的处理函数集,可以描述更加复杂的智能行为;2)Agent-DEVS规范能够较好地描述协作行为。通过模型间的相互协作,可以动态修改知识库中的信息,提高了模型独立处理事务的能力,增强了自治性;3)Agent-DEVS模型实现算法在时间复杂度和执行效率上并不逊色。在计算时间上,该算法仅在最内层原子模型中增加了Agent处理的步骤,对整体复杂性和运行速度方面的影响不大,执行效率与DEVS/CD++基本相当。②研究了基于HLA的Agent-DEVS协同仿真建模方法。提出了基于HLA的Agent-DEVS联邦模型的形式化描述,将Agent-DEVS模型端口转换为HLA数据对象,确定了Agent-DEVS联邦模型结构,并分析了通信机制,分别建立了Agent-DEVS的知识更新与HLA的属性更新,及Agent-DEVS的模型耦合与HLA的实例交互间的映射,设计了Agent-DEVS联邦模型在HLA中的仿真流程。仿真试验结果表明:1)HLA体系框架增强了Agent-DEVS模型的可重用性。可以同时实现与Agent-DEVS模型、非Agent-DEVS模型间的分布式交互;2)知识更新机制丰富了Agent-DEVS模型的互操作性。将知识库的更新从模型耦合的交互中分离出来,避免了耦合交互时的密集数据流量,提高了知识库更新效率;3)知识库的实时性提高了Agent-DEVS模型的独立事务处理能力,自治性进一步增强。③建立了基于SVM的Agent-DEVS模型的参数优化模型。提出了基于SVM的Agent-DEVS模型参数优化的流程,着重针对抢险救灾物资保障仿真模型中的主要参数建立了优化模型,重点分析了数据提取与预处理、核函数选择、SVM参数优选等关键技术,并与BP神经网络进行了对比试验。仿真试验结果表明:1)SVM学习方法增强了Agent-DEVS模型的动态性。通过较少的样本数据较好地预测了模型参数,达到了参数优化的目的;2)自学习能力使Agent-DEVS模型的智能性进一步提高。机器学习方法可以处理更为复杂的智能行为,随着模型的深入应用和样本累积,预测能力将自动增强,具有较充分的自治性;3)优化后的参数使Agent-DEVS模型的描述能力更加精细。将优化后的参数作为模型运行的依据,可以提高计算精度,增强描述对象的精细化程度。④将联合仿真建模技术应用于抢险救灾领域的模拟训练,构建了抢险救灾物资保障模拟训练原型系统。给出了系统建设目标与性能目标,从物资保障业务内容与组织实施过程两方面分析了抢险救灾业务,并由此提出了系统功能需求;阐述了三层系统结构,给出了基于联合仿真建模技术的仿真模型设计流程,并完成了基于HLA的分布式系统设计;分别建立了模拟训练系统评估指标体系与模糊综合评估模型,并对原型系统中的抢险救灾野营物资保障模拟训练子系统进行了评估分析。评估结果表明:该子系统的性能质量等级为“良”。更多还原

【Abstract】 Complex adaptive system is usually composed of many parallel and adaptive agents and provided with hierarchical structure, intelligence and self-adaptability. Emergence is one typical feature of such systems. So it is hard to parse and validate complex adaptive sysem by formalization. Simulation is a feasible approach to this goal, which can formally describe and simulate these systems using distributed and collaborative models. It is important for domain experts to study behaviors and characteristics of a system and to forecast its changes and trends. Similarly, it is significant for a skilled person to engage in operational action, training and rehearsing within the system.At present, agent/swarm is regarded as basic unit in modeling and simulation based on the theory of complex adaptive system, which makes it convenient to model small scale systems and keep high simulation speed. But with the increasing of system scale, interactions among numerous of agents pose higher requirements for the simulation. So the distributed and interactive simulation must be further perfected. Swarm is based on event queue, which can not adequately simulate the randomness of event among simulation agents. Thus, more efficient method is needed to implement the collaboration among simulation objects. Traditional mathematics models can not properly describe changes of complex situation and evolutionary mechanism. Thereby, the dynamics of system should be further depicted. The individual autonomy of agent is mainly interested in swarm. However, centralized control and horizontal ties for different groups are not described in depth. Consequently, the hierarchy and modularity of system ought to be intensively depicted.In order to solve the above-mentioned problems, a technical framework of combining modeling and simulation was proposed by use of the fundamental theory of complex adaptive system. The modeling method based on Agent was introduced into DEVS formalism, which has the advantages of hierarchy and modularity. As a result, this formalism was extended to Agent-DEVS formalism. The distributed and interactive co-simulation was carried out based on HLA. And the parameter optimization of Agent-DEVS model was accomplished based on SVM. Ultimately, a prototype system of simulation training for material supply in emergent disaster was constructed. Generally, main contributions of this thesis are shown as follows: ①Agent-DEVS, an extended DEVS formalism with description capabilities of intelligence and cooperation, was proposed. It was extended based on Parallel-DEVS. State element was expanded to Agent personality element. Agent model element was added to represent intelligent individual behavior. And the input and output of model ports were extended to Agent message to represent social cooperation. Formal specifications of Agent-DEVS atomic model and coupled model were analyzed. The closure of Agent-DEVS models under coupling was proved. And an implementation algorithm of Agent-DEVS models was given. The results of simulation test show that: 1) Agent-DEVS formalism can describe intelligent behavior directly. Main merit of Agent-DEVS is modeling performance. It translates all kinds of parameters into variables stored in knowledge and constructs relevant processing function set, which can describe more complex intelligent behavior. 2) Agent-DEVS formalism can properly describe cooperative behavior. It may dynamically modify the information in knowledge through mutual cooperation among models, which improves the abilities of models for dealing with transactions independently and enhances the autonomy of models. 3) The implementation algorithm of Agent-DEVS models is by no means inferior in the time complexity and modeling efficiency. If considered in computational time, this algorithm only increases the steps for Agent operations in inmost atomic models, which has less impact on the whole complexity and runtime. In terms of modeling efficiency, it is equivalent to that of DEVS/CD++.②Collaborative modeling and simulation method for Agent-DEVS based on HLA was studied. Formal description of Agent-DEVS federate models based on HLA was proposed. All ports of Agent-DEVS models were translated into data objects of HLA. Structure of Agent-DEVS federate models was showed and communication mechanism was analyzed. The mapping between knowledge update in Agent-DEVS and attribute update in HLA, and that between models coupling in Agent-DEVS and instances interaction in HLA, were established respectively. Simulation flow of Agent-DEVS federate models in HLA was designed. The results of simulation test show that: 1) HLA enhances the reusability of Agent-DEVS models. Distributed interaction between Agent-DEVS models and that between Agent-DEVS model and non Agent-DEVS model may be implemented at the same time. 2) Knowledge update mechanism enriches the interoperability of Agent-DEVS models. The update of knowledge repository is separated from interaction of models coupling, which may avoid bulk data transfering in coupling interactions and improve update efficiency of knowledge repository. 3) Independent ability for transaction processing of Agent-DEVS models is improved by real-time knowledge. And the autonomy of Agent-DEVS models is enhanced significantly.③Optimization model for parameters in Agent-DEVS models based on SVM was constructed. Parameter optimization flow of Agent-DEVS models based on SVM was put forward. And the optimization model for main parameter in simulation models of material supply in emergent disaster was established. Some key techniques, such as collection and pretreatment of data, selection of kernel function and optimization selection of SVM parameters, were mainly analyzed. And the comparison with BP neural networks was examined. The results of simulation test show that: 1) SVM enhances the dynamics of Agent-DEVS models. Parameters in the models can be appropriately forecasted by a few samples. So the objective of parameter optimization is reached. 2) Self-learning ability improves the intelligence of Agent-DEVS models significantly. Method of machine learning may handle more complex intelligent behavior. With further application of models and accumulation of samples, the forecast ability will be advanced automatically. It is provided with strong autonomy. 3) Optimized parameters make Agent-DEVS models more elaborate descriptive ability. These parameters are regarded as running basis of models, which may raise the computational precision and increase the level of detail in descriptive object.④The technology of combining modeling and simulation was applied to simulation training in emergent disaster, and the prototype system of simulation training for material supply in emergent disaster was constructed. Construction and performance objectives were brought forward. Operation details and processes of material supply in emergent disaster were analyzed. Then function requirements of the system were proposed. A three-layered system structure was expatiated on. Designing flow of simulation models based on technology of combining modeling and simulation was given. And distributed system design based on HLA was completed. Evaluation indicator system and fuzzy comprehensive evaluation model for simulation training system were presented respectively. And the subsystem of simulation training for camping material supply in emergent disaster was estimated and analyzed. The results of evaluation show that the performance and quality grade of this subsystem is good.更多还原