基于多Agent协调的资源调配研究
摘要
资源分配是人类活动中一类常见而重要的决策活动。例如,工程计划制订就是这样的一类决策活动,它有两个主要特点:一是工程计划由众多专家、技术人员共同决策;二是必须考虑在资源有限等约束条件下完成计划。这一问题中广泛而复杂的协调活动亟需理论、方法的指导和实施技术的支持。传统方法往往将其转化为规划问题,需要仲裁者或者决策者收集所有相关信息,进行集中运算,不仅求解工作量大,不适应现代计算的负债均衡、高容错性等要求,而且决策变量的个数在建模之后就固定了,缺乏应有的灵活性。因此,很难在信息不完全和开放的实际环境下得到应用。
Internet技术的普及使得基于软件Agent的协调支持成为新的研究热点。目前已出现了一些简单情形中的应用原型,如会议或访问日程的安排等。一般说来,对于协调的需求是由于资源、实体、信息的分布特性以及它们之间的相互依赖而产生的。资源分配群体决策的特点恰好具有这样的特性及依赖关系。首先,决策所需的知识、信息是分布在各个决策者之中,决策者一般很难看到问题的全面视图;其次,决策者主观能力各不相同,因此感知得到的信息可能不完全、不准确;第三,决策者对于同一目标的各个决策选项的偏好不尽相同,他们对目标的选择一般难以摆脱自身利益的影响,因而即使在总体目标明确的情况下仍然存在子目标不尽一致的可能性;最后,由于现实世界中存在私有信息如专业知识,使得完全的信息交换是非常困难的。MAS协调可以容许信息分布、信息不精确、个体自治等几项特性,比任何其他方法更适用于群体决策。本文从多Agent系统的视角出发,对资源约束下的计划整合问题进行了较为深入的探讨。
本文就研究基础和背景作了综述,包括MAS协调的相关原理,已有的方法和相关技术,以及MAS协调的相关应用等等,其中,MAS协调的基础理论主要从Agent/MAS的研究现状、MAS中的协调和协调模型两个方面进行了阐述;Agent/MAS的研究现状主要介绍了一些已有的理论、方法和技术,分析了各自的特点;MAS协调和协调模型则主要描述了协调研究的概况,对主要的几种协调模型进行了简单分析。并且说明了研究资源约束的MAS协调的意义和内容。
个体理性是社会智能的基础。在对Agent进行本质和特性分析的基础上,论文重点提出了基于知识水平的协调推理,阐述了基于BDI框架建立个体理性的方法。与传统方法相比,个体理性的构造更容易适应开放式复杂系统的局部、不精确的信息,而且具有很好的动态性、健壮性,也更易于建模、设计与实现。
现代人工智能的特点是社会智能,个体的理性在群体交互中集聚为更高层次、更复杂的智能,即使是再复杂的个体没有良好的社会交互支持,也难以完成复杂的任务。因此,论文把多Agent交互及协调博弈作为研究重点,建立了交互的简单模型并通过它分析了协调产生的环境和背景,从协调博弈理论上讨论了避免协调失败的方法,并且给出了相关的实施技术的形式化表述,为构建MAS资源约束的多计划协调提供了基础。
基于MAS和Agent的设计易于理解和实现,具备负载分布性、容错性,在动态环境中具有很强的适应性,这些都更适用于资源调配的实际工程应用。本文在综合前面论及的个体理性和MAS协商协调两方面理论的基础之上,针对资源有限条件下的计划整合问题,探讨了基于MAS协调的资源调配方法。首先分析了资源有限情况下多个并行计划同时运作的环境,然后描述了该环境下的Agent协商模型和通信模型。由于个体决策取决于Agent所代表的用户,着重讨论了这一方法中的个体决策模型,提出了MAReco (Multi-Agent Resource Coordination)这一基于MAS仿真的资源约束多计划整合模型,在三个合理假设——个体理性,信息交换,MAS协商——的基础上,得出了形式化模型和通过社会选择协商求解的方法。
本文还以MAReCo推广应用可能遇到的困难为出发点,进一步阐述了资源约束的计划整合中MAS协调的应用背景,并且从系统构架、协调对象的确定、协调规则的设计等方面,比较详细地探讨了如何将MAReCo应用于具体的运输调度系统的构造。通过数据试验,验证了基于MAReco的MATs(Multi-Agent Transit Scheduling)在单阶段计划、多阶段计划两种情况下的可行性和效果。
本文所做基于MAS协调的资源调配研究,对于供应链管理、智能交通系统、智能软件体系的研究与应用均有重要的理论意义和参考价值。
Resource allocation is an important type of decision making activity in project planning. Actually, group decision in project planning has two attributes. Firstly, the plan is made by many specialists together. Secondly, the plan must be executable under constraints as resource limitation. It is urgently needed of theory and method to support the coordination in this kind of problem. The traditional approach generally models this problem with mathematic programming, where the central decision-maker has to collect all information, and the model can hardly be changed once it is constructed. It has a lot of limitation in practice. Especially it is not easy to be applied in the open environment where information is incomplete and inaccurate.
The swift prevalence of internet has made coordination based on software agent being a new research hotspot. There have been some simple applications like schedule management. The demand for coordination comes from the distributation of resources, entities, information and the interdependencies among them. The characteristics of group decision on resource allocation just bring this kind of properties and interdependencies. Firstly, the knowledge and information needed in the decision are distributed among each decision-maker; therefore no one can see the global view of the decision problem easily. Secondly, the decision-makers’specialities and capabilities are various, so the percepted information are different and even inaccurate. Thirdly, the decision-makers’preference are unlike and their choices of objective are dependent on their own preference. Lastly, there exist privacies in reality, which make it hard to exchange all information. Thus, MAS coordination is more applicable to group decision. This dissertation studied the MAS coordination applied in project plan integration with resource constraint.
In the first place, the research basis and background is introduced, which include theories of MAS coordination, current methods, technologies and applications. The theories of MAS coordination is set forth from the review of agent and MAS study, coordination model. Current theories, methods and technologies are introduces, and their properties are analysed. The several mainstream coordination models are also discussed. The content and significance are proposed as the end of introduction.
Individual rationality is a foundation of social intelligence. Based on the analysis of agents’characteristics and nature, several mainstream infrastructures of agent are discussed, especially the BDI framework. Then a new method to construct individual rationality is presented. Compared with traditional approach, this method is more applicable to open complex system with its dynamical and robust properties.
Modern AI is focused on social intelligence. The individual rationalities converge on higher and complicated intelligence during social interaction. Without right support of social interaction, even more complicated individuals can hardly complete complex task. Thus, this dissertation treats multi agent interaction and coordination game as keystone. From the simplest interaction to corperation, each level of interactions between agents is analysed. The communication in interaction is also introduced. Then, coordination method based on coordination knowledge level is discussed. The coordination model is analysed using coordination game.
Design based on agent and MAS is easy to be understood and implemented, and has merits such as load-balance, fault-tolarence, and adapted to dynamical context, which are more applicable in practical project resource allocation. Based on forementioned individual rationalities and social interaction, this dissertation focused on resource constrained multi-plans integration problem, and exploit a new method of resource allocation. Firstly the context where multi-plans run simultenously with limited resources is analysed. Then coordination model and agent communication are described. Since individual decisions is affected by the users that agent representats, individual decision making process is emphasized. MAReCo is proposed based on these thought. With three reasonable assumptions, namely individual rationality, information exchange and MAS negotiation, a formal description and solving approach are presented.
To the implementation and practice of MAReCo, this dissertation taking transit scheduling for example, set forth the application background, system architecture, design of coordination rules and coordination objects, and MATs is constructe. Through numerical test, the feasibility and effectiveness of MATs is verified.
This study on resource allocation has significant value to supply chain management, intelligent transportation system, and intelligent software.
Internet技术的普及使得基于软件Agent的协调支持成为新的研究热点。目前已出现了一些简单情形中的应用原型,如会议或访问日程的安排等。一般说来,对于协调的需求是由于资源、实体、信息的分布特性以及它们之间的相互依赖而产生的。资源分配群体决策的特点恰好具有这样的特性及依赖关系。首先,决策所需的知识、信息是分布在各个决策者之中,决策者一般很难看到问题的全面视图;其次,决策者主观能力各不相同,因此感知得到的信息可能不完全、不准确;第三,决策者对于同一目标的各个决策选项的偏好不尽相同,他们对目标的选择一般难以摆脱自身利益的影响,因而即使在总体目标明确的情况下仍然存在子目标不尽一致的可能性;最后,由于现实世界中存在私有信息如专业知识,使得完全的信息交换是非常困难的。MAS协调可以容许信息分布、信息不精确、个体自治等几项特性,比任何其他方法更适用于群体决策。本文从多Agent系统的视角出发,对资源约束下的计划整合问题进行了较为深入的探讨。
本文就研究基础和背景作了综述,包括MAS协调的相关原理,已有的方法和相关技术,以及MAS协调的相关应用等等,其中,MAS协调的基础理论主要从Agent/MAS的研究现状、MAS中的协调和协调模型两个方面进行了阐述;Agent/MAS的研究现状主要介绍了一些已有的理论、方法和技术,分析了各自的特点;MAS协调和协调模型则主要描述了协调研究的概况,对主要的几种协调模型进行了简单分析。并且说明了研究资源约束的MAS协调的意义和内容。
个体理性是社会智能的基础。在对Agent进行本质和特性分析的基础上,论文重点提出了基于知识水平的协调推理,阐述了基于BDI框架建立个体理性的方法。与传统方法相比,个体理性的构造更容易适应开放式复杂系统的局部、不精确的信息,而且具有很好的动态性、健壮性,也更易于建模、设计与实现。
现代人工智能的特点是社会智能,个体的理性在群体交互中集聚为更高层次、更复杂的智能,即使是再复杂的个体没有良好的社会交互支持,也难以完成复杂的任务。因此,论文把多Agent交互及协调博弈作为研究重点,建立了交互的简单模型并通过它分析了协调产生的环境和背景,从协调博弈理论上讨论了避免协调失败的方法,并且给出了相关的实施技术的形式化表述,为构建MAS资源约束的多计划协调提供了基础。
基于MAS和Agent的设计易于理解和实现,具备负载分布性、容错性,在动态环境中具有很强的适应性,这些都更适用于资源调配的实际工程应用。本文在综合前面论及的个体理性和MAS协商协调两方面理论的基础之上,针对资源有限条件下的计划整合问题,探讨了基于MAS协调的资源调配方法。首先分析了资源有限情况下多个并行计划同时运作的环境,然后描述了该环境下的Agent协商模型和通信模型。由于个体决策取决于Agent所代表的用户,着重讨论了这一方法中的个体决策模型,提出了MAReco (Multi-Agent Resource Coordination)这一基于MAS仿真的资源约束多计划整合模型,在三个合理假设——个体理性,信息交换,MAS协商——的基础上,得出了形式化模型和通过社会选择协商求解的方法。
本文还以MAReCo推广应用可能遇到的困难为出发点,进一步阐述了资源约束的计划整合中MAS协调的应用背景,并且从系统构架、协调对象的确定、协调规则的设计等方面,比较详细地探讨了如何将MAReCo应用于具体的运输调度系统的构造。通过数据试验,验证了基于MAReco的MATs(Multi-Agent Transit Scheduling)在单阶段计划、多阶段计划两种情况下的可行性和效果。
本文所做基于MAS协调的资源调配研究,对于供应链管理、智能交通系统、智能软件体系的研究与应用均有重要的理论意义和参考价值。
Resource allocation is an important type of decision making activity in project planning. Actually, group decision in project planning has two attributes. Firstly, the plan is made by many specialists together. Secondly, the plan must be executable under constraints as resource limitation. It is urgently needed of theory and method to support the coordination in this kind of problem. The traditional approach generally models this problem with mathematic programming, where the central decision-maker has to collect all information, and the model can hardly be changed once it is constructed. It has a lot of limitation in practice. Especially it is not easy to be applied in the open environment where information is incomplete and inaccurate.
The swift prevalence of internet has made coordination based on software agent being a new research hotspot. There have been some simple applications like schedule management. The demand for coordination comes from the distributation of resources, entities, information and the interdependencies among them. The characteristics of group decision on resource allocation just bring this kind of properties and interdependencies. Firstly, the knowledge and information needed in the decision are distributed among each decision-maker; therefore no one can see the global view of the decision problem easily. Secondly, the decision-makers’specialities and capabilities are various, so the percepted information are different and even inaccurate. Thirdly, the decision-makers’preference are unlike and their choices of objective are dependent on their own preference. Lastly, there exist privacies in reality, which make it hard to exchange all information. Thus, MAS coordination is more applicable to group decision. This dissertation studied the MAS coordination applied in project plan integration with resource constraint.
In the first place, the research basis and background is introduced, which include theories of MAS coordination, current methods, technologies and applications. The theories of MAS coordination is set forth from the review of agent and MAS study, coordination model. Current theories, methods and technologies are introduces, and their properties are analysed. The several mainstream coordination models are also discussed. The content and significance are proposed as the end of introduction.
Individual rationality is a foundation of social intelligence. Based on the analysis of agents’characteristics and nature, several mainstream infrastructures of agent are discussed, especially the BDI framework. Then a new method to construct individual rationality is presented. Compared with traditional approach, this method is more applicable to open complex system with its dynamical and robust properties.
Modern AI is focused on social intelligence. The individual rationalities converge on higher and complicated intelligence during social interaction. Without right support of social interaction, even more complicated individuals can hardly complete complex task. Thus, this dissertation treats multi agent interaction and coordination game as keystone. From the simplest interaction to corperation, each level of interactions between agents is analysed. The communication in interaction is also introduced. Then, coordination method based on coordination knowledge level is discussed. The coordination model is analysed using coordination game.
Design based on agent and MAS is easy to be understood and implemented, and has merits such as load-balance, fault-tolarence, and adapted to dynamical context, which are more applicable in practical project resource allocation. Based on forementioned individual rationalities and social interaction, this dissertation focused on resource constrained multi-plans integration problem, and exploit a new method of resource allocation. Firstly the context where multi-plans run simultenously with limited resources is analysed. Then coordination model and agent communication are described. Since individual decisions is affected by the users that agent representats, individual decision making process is emphasized. MAReCo is proposed based on these thought. With three reasonable assumptions, namely individual rationality, information exchange and MAS negotiation, a formal description and solving approach are presented.
To the implementation and practice of MAReCo, this dissertation taking transit scheduling for example, set forth the application background, system architecture, design of coordination rules and coordination objects, and MATs is constructe. Through numerical test, the feasibility and effectiveness of MATs is verified.
This study on resource allocation has significant value to supply chain management, intelligent transportation system, and intelligent software.
引文
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