面向复杂决策问题的模型构造与管理方法研究
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摘要
问题求解是人工智能、管理决策等领域中的一个研究热点。在管理决策领域,主要是基于DSS进行问题求解理论的研究。DSS是利用模型和数据,以人机交互方式辅助决策者处理半结构化和非结构化问题的、是支持而不是代替管理者作出判断的、是提高决策的有效性而不是效率的基于计算机的系统。因此,DSS的目的是辅助决策者解决问题。然而,解决问题必须依靠求解问题的模型,求解问题的数据需求也是由模型确定的。因此,模型是DSS的核心,DSS的运行是由模型驱动的,模型构造、表示与管理等模型相关理论对问题求解的研究起到至关重要的作用。
随着科学技术和经济社会的巨大发展,决策问题变得越发复杂。复杂决策问题所具有的非线性、变结构和变参数等特性对模型相关理论的研究提出了更高的要求。已有的模型相关理论在用于日益复杂化的决策问题求解时,总是存在着这样或那样的不足。基于这一背景,本文在前人研究成果的基础上,对决策支持模型的构造、表示与管理等模型相关理论作进一步深入的探讨。具体内容包括:
(1)探讨了复杂决策问题求解的一般过程,包括问题定义阶段、建模阶段、问题求解阶段以及解的解释与评价阶段。在复杂决策问题求解一般过程的基础上,给出了复杂决策问题求解过程的形式化描述,并初步提出了复杂决策问题求解过程中涉及的各种知识空间以及知识空间的映射关系。研究了定性知识与定量知识在复杂问题求解过程中的认识不完备性,提出了面向复杂决策问题求解的多元化模型体系,初步阐述了问题结构模型、问题关系描述模型以及问题求解模型的概念。
(2)借鉴M-S方法的基本思想,并基于对复杂决策问题求解过程的深入理解,提出了一种定性定量相结合的综合集成模型构造方法。该方法依据面向复杂决策问题求解的多元化模型体系,将模型构造过程分为三个阶段,分别对应问题结构模型、问题关系描述模型以及问题求解模型的构造过程。同时,综合运用领域知识、问题知识、模型知识、方法知识以及工具、算子类知识等指导三个阶段的构模过程,体现了定性定量相结合的综合集成思想。
(3)借鉴面向对象思想,给出了模型的层次化逻辑表示框架。框架从模型类型、模型实例、模型元件以及模型元素4个方面完整地给出了模型的表示方法。模型类型定义了模型分类体系以及建模过程的一般模式;模型实例是问题相关的模型,是特定问题的完整表示,与问题求解过程紧密相关,是模型类型的实例化;模型元件是指将模型实例分解而成的细小的模型单元,每一个模型元件都由模型变量及其相应的操作组成;模型元素包含了模型的所有细节,如变量、参数以及操作函数,它们是构造模型元件的基本对象。基于模型的层次化逻辑表示框架,探讨了基于模型元素的模型元件的生成方法以及模型元件的集成方法;给出了基于深度优先搜索的模型求解链生成算法,以及基于满意策略的模型选择算法。
(4)给出了基于关系型数据库的模型存储与管理层次化结构。该结构由基础模型层、构件模型层以及概念模型层组成。基础模型层实现了模型层次表示框架的模型元素、模型元件、模型实例以及模型类型的物理存储与管理问题,用于支持模型构造者;构件模型层封装了基础模型的细节部分,通过接口向外界提供模型的基本信息,增强了模型的重用性,用于支持DSS构造者;概念模型层通过给出决策问题的形式化表示,建立了决策问题与模型构件的联系,用于支持决策者或决策分析人员。
(5)给出了基于Multi-Agent的多模型集成问题求解方法中的Agent的分类体系,将Agent分为管理Agent、任务Agent和模型Agent。给出了模型Agent的构造方法,包括基于E-R-P知识表示体系的模型Agent构造以及利用Agent外壳封装模型软件两种方法。探讨了基于Multi-Agent的多模型集成问题求解方法的具体流程。
(6)研究开发了一个模型管理原型系统,给出了原型系统的体系结构框架,并阐述了原型系统各组成部分的功能与实现方法。探讨了该模型管理原型系统在国家“十五”重点科技攻关项目——“面向行政区域的国民经济与社会发展的辅助决策支持技术的应用”中的具体应用。设计与实现了区域的国民经济决策分析模型与组件,其中包括组合时间序列模型、基于神经元网络的组合多元仿真模型、空间分布式消费需求分析模型、空间分布式人口仿真模型,并探讨了决策分析问题管理与组件集成元数据管理。通过给出一个国内生产总值预测分析的例子,探讨了运用本模型系统求解复杂决策问题的具体过程,并通过对预测结果的分析,论证了本模型系统的科学性与实用性。探讨了试点区域辅助决策支持技术的应用问题,包括国务院西部大开发辅助决策应用与重庆市试点应用。
Problem solving is a hot issue in the research field of artificial intelligence and management decision. In the field of management decision, the research for theory of problem solving is mainly based on DSS. DSS supports decision makers to solve semi-structural and non-structural problems in the way of man-machine interaction by making use of models and data. It is such a computer system that supporting but not substituting decision makers to make judgments and heightening the effectivity but not efficiency of decision. Therefore, the purpose of DSS is to support decision makers to solve problems. However, problem solving must be with the aid of the models, which could be used to solve problems. Furthermore, the data requirement of problem solving is also determined by models. So model is the core of DSS. The running of DSS is driven by models. And the relational theories of model construction, representation and management are vital to the research of problem solving.
With the development of science and technology and social economy, the decision-making problems become more and more complex. The features of nonlinearity, variable structure and variable parameter of complex decision-making problem lay higher claim to the research on the theories relating to model. The exist theories relating to model always appear insufficient while being used in solving increasingly complex decision-making problems. On the basis of these backgrounds, the theories relating to model construction, representation, management, etc. are discussed further and in depth on the basis of research fruits of people of the past. The specific content is as follows.
(1) The general solving process of complex decision-making problem, which includes problem definition phase, modeling phase, problem solving phase and phase for explanation and evaluation of solution is given. The formal description for solving process of complex decision-making problem is given based on the general solving process of complex decision-making problem. All kinds of knowledge spaces involved in the solving process of complex decision-making problem and the maps between the knowledge spaces are described formally. The incompleteness of qualitative knowledge and quantitative knowledge when being used in solving complex decision-making problem is researched. Based on the incompleteness research, a multifaceted model system of complex decision_making problem solving is proposed. And the concepts for structure model, relation description model and solving model of complex decision-making problem in the multifaceted model system are illustrated preliminarily.
(2) A meta-synthesis approach to modeling orienting complex decision-making problem solving is proposed based on the in-depth comprehension to the solving process complex decision-making problem and the basic thinking of meta-synthesis approach. In this approach, the modeling process is divided into three phases, which correspond to the construction process of structure model, relation description model and solving model of complex decision-making problem, based on the multifaceted model system of complex decision_making problem solving. At the same time, the domain knowledge and the problem knowledge, which are qualitative, and the model knowledge, the method knowledge, the tool knowledge and the operator knowledge, which are quantitative, are used synthetically to support these three modeling phases.
(3) A hierarchical logic representation framework of models is given by drawing lessons from object-oriented thinking. This framework gives a complete representation approach to models in terms of model type, model instance, model component and model element. Model type defines a general pattern of model taxonomy and modeling capabilities. Model instance refers to individual problem-specific models. It is in correlate with the process of problem solving closely and is the instantiation of model type. Model component aims to decompose individual model instance into parts, which are small model units with certain basic functions, parameters and procedures. Each component comprises some model variables and their associated actions. Model element includes all the details of a specific model instance, such as variables, parameters and simple mathematical functions. They are basic objects used to construct model components. On the basis of the hierarchical logic framework, the construction approach of model component based on model element and the integration approach of model component are discussed. The algorithm for constructing model solving chain based on the depth-first search algorithm and the algorithm for model selection based on the satisficing strategy are put forward.
(4) A hierarchical pattern for model storage and management based on relational database is put forward. This pattern is made up of basic layer, building block layer and conceptual layer. The basic layer describes the physical storage and management pattern of the model element, the model component, the model instance and the model type in the hierarchical framework of model representation. It can be used to support constructors of decision models. The building block layer encapsulates the details of basic decision models and supplies the basic information of decision models through interfaces of model building blocks. This enhances the reusability of decision models. The building block layer can be used to support the constructors of DSS. The conceptual layer establishes the connections between decision-making problems and model building blocks through giving the formal representation of decision-making problem. It can be used to support the decision makers and analyzers.
(5) The taxonomy for agent in the problem solving method of multi-model cooperation based on Multi-Agent is given. Then, the approach to the construction of model agent, which includes direct development approach and the approach of using agent shell to encapsulate model software, is put forward. Additionally, the concrete problem solving flow of multi-model cooperation based on Multi-Agent is discussed.
(6) A prototypical system for model management is researched and developed. The architecture as well as the function and implementation method of the components for the prototypical system are described. The concrete application of the prototypical system in the application of decision support technology of national economy and society development that of administrative area oriented, which is national key scientific and technological project of the tenth five-year, is discussed. Some models and components, including combinational time sequence model, combinational and multivariate emulation model based on neural network, spatial and distributed consumption requirement analysis model and spatial and distributed population simulation model, for decision analysis of regional national economy are designed and implemented. Then, the problem management for decision analysis and metadata management for component integration are discussed. Additionally, by means of an example that predicting the gross domestic product, the specific solving process of complex decision-making problem based on the model system is discussed. Furthermore, through the analysis of prediction result, the scientificity and practicability is demonstrated. At last, the application for decision support technology of pilot regions, which include decision technology application of west China development and pilot application of Chongqing city, is discussed.
随着科学技术和经济社会的巨大发展,决策问题变得越发复杂。复杂决策问题所具有的非线性、变结构和变参数等特性对模型相关理论的研究提出了更高的要求。已有的模型相关理论在用于日益复杂化的决策问题求解时,总是存在着这样或那样的不足。基于这一背景,本文在前人研究成果的基础上,对决策支持模型的构造、表示与管理等模型相关理论作进一步深入的探讨。具体内容包括:
(1)探讨了复杂决策问题求解的一般过程,包括问题定义阶段、建模阶段、问题求解阶段以及解的解释与评价阶段。在复杂决策问题求解一般过程的基础上,给出了复杂决策问题求解过程的形式化描述,并初步提出了复杂决策问题求解过程中涉及的各种知识空间以及知识空间的映射关系。研究了定性知识与定量知识在复杂问题求解过程中的认识不完备性,提出了面向复杂决策问题求解的多元化模型体系,初步阐述了问题结构模型、问题关系描述模型以及问题求解模型的概念。
(2)借鉴M-S方法的基本思想,并基于对复杂决策问题求解过程的深入理解,提出了一种定性定量相结合的综合集成模型构造方法。该方法依据面向复杂决策问题求解的多元化模型体系,将模型构造过程分为三个阶段,分别对应问题结构模型、问题关系描述模型以及问题求解模型的构造过程。同时,综合运用领域知识、问题知识、模型知识、方法知识以及工具、算子类知识等指导三个阶段的构模过程,体现了定性定量相结合的综合集成思想。
(3)借鉴面向对象思想,给出了模型的层次化逻辑表示框架。框架从模型类型、模型实例、模型元件以及模型元素4个方面完整地给出了模型的表示方法。模型类型定义了模型分类体系以及建模过程的一般模式;模型实例是问题相关的模型,是特定问题的完整表示,与问题求解过程紧密相关,是模型类型的实例化;模型元件是指将模型实例分解而成的细小的模型单元,每一个模型元件都由模型变量及其相应的操作组成;模型元素包含了模型的所有细节,如变量、参数以及操作函数,它们是构造模型元件的基本对象。基于模型的层次化逻辑表示框架,探讨了基于模型元素的模型元件的生成方法以及模型元件的集成方法;给出了基于深度优先搜索的模型求解链生成算法,以及基于满意策略的模型选择算法。
(4)给出了基于关系型数据库的模型存储与管理层次化结构。该结构由基础模型层、构件模型层以及概念模型层组成。基础模型层实现了模型层次表示框架的模型元素、模型元件、模型实例以及模型类型的物理存储与管理问题,用于支持模型构造者;构件模型层封装了基础模型的细节部分,通过接口向外界提供模型的基本信息,增强了模型的重用性,用于支持DSS构造者;概念模型层通过给出决策问题的形式化表示,建立了决策问题与模型构件的联系,用于支持决策者或决策分析人员。
(5)给出了基于Multi-Agent的多模型集成问题求解方法中的Agent的分类体系,将Agent分为管理Agent、任务Agent和模型Agent。给出了模型Agent的构造方法,包括基于E-R-P知识表示体系的模型Agent构造以及利用Agent外壳封装模型软件两种方法。探讨了基于Multi-Agent的多模型集成问题求解方法的具体流程。
(6)研究开发了一个模型管理原型系统,给出了原型系统的体系结构框架,并阐述了原型系统各组成部分的功能与实现方法。探讨了该模型管理原型系统在国家“十五”重点科技攻关项目——“面向行政区域的国民经济与社会发展的辅助决策支持技术的应用”中的具体应用。设计与实现了区域的国民经济决策分析模型与组件,其中包括组合时间序列模型、基于神经元网络的组合多元仿真模型、空间分布式消费需求分析模型、空间分布式人口仿真模型,并探讨了决策分析问题管理与组件集成元数据管理。通过给出一个国内生产总值预测分析的例子,探讨了运用本模型系统求解复杂决策问题的具体过程,并通过对预测结果的分析,论证了本模型系统的科学性与实用性。探讨了试点区域辅助决策支持技术的应用问题,包括国务院西部大开发辅助决策应用与重庆市试点应用。
Problem solving is a hot issue in the research field of artificial intelligence and management decision. In the field of management decision, the research for theory of problem solving is mainly based on DSS. DSS supports decision makers to solve semi-structural and non-structural problems in the way of man-machine interaction by making use of models and data. It is such a computer system that supporting but not substituting decision makers to make judgments and heightening the effectivity but not efficiency of decision. Therefore, the purpose of DSS is to support decision makers to solve problems. However, problem solving must be with the aid of the models, which could be used to solve problems. Furthermore, the data requirement of problem solving is also determined by models. So model is the core of DSS. The running of DSS is driven by models. And the relational theories of model construction, representation and management are vital to the research of problem solving.
With the development of science and technology and social economy, the decision-making problems become more and more complex. The features of nonlinearity, variable structure and variable parameter of complex decision-making problem lay higher claim to the research on the theories relating to model. The exist theories relating to model always appear insufficient while being used in solving increasingly complex decision-making problems. On the basis of these backgrounds, the theories relating to model construction, representation, management, etc. are discussed further and in depth on the basis of research fruits of people of the past. The specific content is as follows.
(1) The general solving process of complex decision-making problem, which includes problem definition phase, modeling phase, problem solving phase and phase for explanation and evaluation of solution is given. The formal description for solving process of complex decision-making problem is given based on the general solving process of complex decision-making problem. All kinds of knowledge spaces involved in the solving process of complex decision-making problem and the maps between the knowledge spaces are described formally. The incompleteness of qualitative knowledge and quantitative knowledge when being used in solving complex decision-making problem is researched. Based on the incompleteness research, a multifaceted model system of complex decision_making problem solving is proposed. And the concepts for structure model, relation description model and solving model of complex decision-making problem in the multifaceted model system are illustrated preliminarily.
(2) A meta-synthesis approach to modeling orienting complex decision-making problem solving is proposed based on the in-depth comprehension to the solving process complex decision-making problem and the basic thinking of meta-synthesis approach. In this approach, the modeling process is divided into three phases, which correspond to the construction process of structure model, relation description model and solving model of complex decision-making problem, based on the multifaceted model system of complex decision_making problem solving. At the same time, the domain knowledge and the problem knowledge, which are qualitative, and the model knowledge, the method knowledge, the tool knowledge and the operator knowledge, which are quantitative, are used synthetically to support these three modeling phases.
(3) A hierarchical logic representation framework of models is given by drawing lessons from object-oriented thinking. This framework gives a complete representation approach to models in terms of model type, model instance, model component and model element. Model type defines a general pattern of model taxonomy and modeling capabilities. Model instance refers to individual problem-specific models. It is in correlate with the process of problem solving closely and is the instantiation of model type. Model component aims to decompose individual model instance into parts, which are small model units with certain basic functions, parameters and procedures. Each component comprises some model variables and their associated actions. Model element includes all the details of a specific model instance, such as variables, parameters and simple mathematical functions. They are basic objects used to construct model components. On the basis of the hierarchical logic framework, the construction approach of model component based on model element and the integration approach of model component are discussed. The algorithm for constructing model solving chain based on the depth-first search algorithm and the algorithm for model selection based on the satisficing strategy are put forward.
(4) A hierarchical pattern for model storage and management based on relational database is put forward. This pattern is made up of basic layer, building block layer and conceptual layer. The basic layer describes the physical storage and management pattern of the model element, the model component, the model instance and the model type in the hierarchical framework of model representation. It can be used to support constructors of decision models. The building block layer encapsulates the details of basic decision models and supplies the basic information of decision models through interfaces of model building blocks. This enhances the reusability of decision models. The building block layer can be used to support the constructors of DSS. The conceptual layer establishes the connections between decision-making problems and model building blocks through giving the formal representation of decision-making problem. It can be used to support the decision makers and analyzers.
(5) The taxonomy for agent in the problem solving method of multi-model cooperation based on Multi-Agent is given. Then, the approach to the construction of model agent, which includes direct development approach and the approach of using agent shell to encapsulate model software, is put forward. Additionally, the concrete problem solving flow of multi-model cooperation based on Multi-Agent is discussed.
(6) A prototypical system for model management is researched and developed. The architecture as well as the function and implementation method of the components for the prototypical system are described. The concrete application of the prototypical system in the application of decision support technology of national economy and society development that of administrative area oriented, which is national key scientific and technological project of the tenth five-year, is discussed. Some models and components, including combinational time sequence model, combinational and multivariate emulation model based on neural network, spatial and distributed consumption requirement analysis model and spatial and distributed population simulation model, for decision analysis of regional national economy are designed and implemented. Then, the problem management for decision analysis and metadata management for component integration are discussed. Additionally, by means of an example that predicting the gross domestic product, the specific solving process of complex decision-making problem based on the model system is discussed. Furthermore, through the analysis of prediction result, the scientificity and practicability is demonstrated. At last, the application for decision support technology of pilot regions, which include decision technology application of west China development and pilot application of Chongqing city, is discussed.
引文
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