考虑主观偏好信息的辐射源威胁评估方法

基于作战实际情况，提出一种新的辐射源威胁评估方法。决策群体针对当前战场情况，凭借其作战经验分别给出不同形式的主观偏好信息。采用将偏好信息一致化为效用值的方法，集结出决策群体的主观偏好信息。建立主客观信息相集成的辐射源威胁评估模型，然后通过二次规划模型计算属性权重，最后通过一个排序公式得出辐射源的威胁评估值和排序结果。所建立的威胁评估模型更符合作战的实际过程，算例表明了方法的有效性。     

基于多智能体的分布式智能诊断方法研究

在传统智能诊断基础上，将多Agent技术应用于动态、分布、实时和不确定的复杂系统故障诊断领域，以求解复杂过程诊断问题．提出了基于MAS的分布式智能故障诊断方法和过程；讨论了基于模式聚类的故障求解机制及对诊断问题任务的辨识、分解；研究了多Agent的约束和关联；设计了应用Agent工作状态的表达机制；确定了应用Agent间的工作状态影响关系及多Agent间的交互、协作和通讯；构建了多Agent模糊关联模型；给出了多Agent诊断系统局部诊断决策与全局诊断决策的集成描述结构：实现了一种分布式Agent诊断系统结构及其原型系统．工程应用表明，该系统能快速、准确地进行故障成因分析，并给出有效的决策意见，取得了与专家相似的诊断结果．     

基于智能Agent的多维权值信息检索模型

为了弥补目前信息检索系统中存在的资源耗费大、实效性不高、用户满意度低等缺点,该文提出并实现一种新型的基于智能Agent的多维权值信息检索模型。该模型主要思想是在信息检索系统中应用了智能Agent技术,通过将检索任务分担到用户客户机、检索服务器和被检索主机的方式以达到提高检索实效性和节省系统和网络资源的目的;该文还通过综合考虑用户检索偏好等特征以及信息本身的重要度和检索匹配程度,提出了一种多维权值排序算法MWRA,提高检索的排序能力,从而为用户提供符合其个性化特征的检索结果。该文首先描述了基于智能Agent的多维权值信息检索模型,然后深入分析了多维权值排序算法MWRA,最后对模型的性能进行了比较和分析。实验结果和性能分析结论表明,基于智能Agent的多维权值信息检索模型的性能尤其在排序能力等方面有明显的提高。     

基于WFPN和多Agent黑板模型的PAAIS知识处理研究

驾驶员辅助决策人工智能系统（PAAIs）是辅助飞机驾驶员进行决策的智能系统，其核心是知识处理。文章采用黑板模型与多Agent技术相结合来构建PAAIS的知识处理架构，以加权模糊Petri网（WFPN）进行模糊知识表示，并提出有效的模糊推理算法。     

基于群决策的多业务无线接入选择方法

在异构无线环境中,为了给运行了多个业务(群呼叫)的移动终端选择一个合适的无线接入网络,提出了一种基于群决策的接入选择方法。该方法引入效用函数对网络QoS及功耗进行评价;根据业务特点分配各属性的模糊权重,并采用业务优先权来反映各业务在群决策中的相对重要性;基于模糊理论,设计合理的模糊TOPSIS数据聚合模型,并在效用评价基础上实现了群决策。仿真结果表明,该方法能有效地实现多业务的无线接入选择,并且业务优先权在很大程度上影响了群决策结果。     

基于群决策的多业务无线接入选择方法

在异构无线环境中,为了给运行了多个业务（群呼叫）的移动终端选择一个合适的无线接入网络,提出了一种基于群决策的接入选择方法。该方法引入效用函数对网络QoS及功耗进行评价;根据业务特点分配各属性的模糊权重,并采用业务优先权来反映各业务在群决策中的相对重要性;基于模糊理论,设计合理的模糊TOPSIS数据聚合模型,并在效用评价基础上实现了群决策。仿真结果表明,该方法能有效地实现多业务的无线接入选择,并且业务优先权在很大程度上影响了群决策结果。     

高速公路模糊综合评价系统的研究与设计

文章基于语言变量的模糊决策方法，构建了某高速公路综合评价系统。在基于语言变量的模糊MCDM问题中。提出了一种新的语言模糊集截集决策方法，在一定的范围内，这种方法不仅能够决策出最佳方案，而且还能给出备选方案间的一个清晰的排序。     

基于黑板的多Agent智能决策支持系统的研究

结合黑板系统及Agent技术,设计了基于黑板的多Agent智能决策支持系统模型,并对该模型的功能模块进行了详细分析,每个Agent利用黑板进行交互,通过黑板控制Agent对黑板进行控制,同时给出各Agent的描述。此外,提出了多个决策Agent协调决策的处理方法,该系统可用于多个Agent协同求解大规模复杂问题。     

基于信息价值的多属性群决策方法

根据信息的质量和价值这两个重要属性,建立了覆盖信息的时效性、关联性和准确性的价值评价指标体系;然后针对多属性群决策问题的描述和求解特点,构建了基于信息评价的相关数学模型,进而计算决策群体对各备选方案的最终排序结果;最后用炮兵防空实例说明其有效性。该方法将信息价值评价的思想引入到群决策,考虑了决策时所获取信息的延时损失及其与决策任务的关联性。     

一种基于Agent复合学习的搜索引擎模型

基于Internet上信息检索中存在的问题,提出了一种基于多Agent的搜索引擎模型,并具体描述该模型的体系结构及其检索机制,描述了用户偏好学习算法。借助Agent复合学习方法对网页信息进行分类学习,从而达到提高检索精度的目的。     

Improving group decision making: a fuzzy GSS approach

Group decision-making methods have been developed extensively, but their adaptation for use in organizations has been problematic. According to Arrow's (1963) Impossibility theorem, one conceivable reason is that a group decision outcome could never satisfy all decision makers' individual preferences. In order to accommodate individual decision maker's preferences in a group decision-making task, this correspondence presents a fuzzy multiperson multicriteria decision making (MMCDM) model and a structured group decision-making process. The fuzzy MMCDM model includes fuzzy individual preference generation and group preference aggregation. The structured decision-making process keeps the group interaction on track, so that the fuzzy MMCDM model can be effectively applied to the group decision-making task. Based on the proposed model and the decision-making process, a fuzzy group support system (GSS) has been developed and applied to a group assessment task. An empirical study was conducted and the experiment results showed that use of the fuzzy GSS enhanced individual understanding, consensus, and satisfaction of the group decision outcome     

Multi‐criteria Evaluation of Mobile Network Sharing Policies in Korea

Mobile operators in saturated markets increasingly favor mobile network sharing (MNS) over facility‐based competition. Previous research examining MNS primarily focused on its positive effects, while the negative effects were largely overlooked. This study proposes a decision‐making model using an analytic hierarchy process technique to evaluate decision‐making criteria among various types of MNS policies. The decision‐making model was applied to Wireless Broadband services in Korea to determine the relative importance of both positive and negative evaluation criteria and preference among multiple types of MNS policies. Positive evaluation criteria (that is, efficiency) were far greater in importance than negative evaluation criteria (that is, competition harm). The preference for adopting MNS among five alternative approaches was also revealed. The study findings offer immediate policy insights in Korea and provide a decision‐making framework for policy makers in other countries to utilize.     

Cost Sensitive Multi-Granulation Preference Relation Rough Set for Ordinal Decision System

The preference analysis is a class of important issues in multi-criteria ordinal decision making. The rough set is an effective approach to handle preference analysis. In order to solve the multi-criteria preference analysis problems, this paper improves the preference relation rough set model and expands it to multi-granulation cases. Cost is also an important issue in the field of decision analysis. Taking the cost into consideration, we also expand the model to the cost sensitive multi-granulation preference relation rough set. Some theorems are represented, and the granule structure selection based on approximation quality is investigated. The experimental results show that the multi-granulation preference rough set approach with the consideration of cost has a better performance in granule structure selection than that without cost consideration.     

Network selection based on multiple attribute decision making and group decision making for heterogeneous wireless networks

One of the key issues for radio resources management is network selection strategy in heterogeneous scenarios.In order to provide ubiquitous service,the paper puts forward a network selection algorithm based on multiple attribute decision making(MADM) and group decision making(GDM).Firstly,the proposed algorithm acquires attribute weights’ vectors of the subjective and objective decision makers based on MADM,and then the two attribute weights’ vectors are synthesized to be a new attribute weights’ vector by using GDM.Considering that the results of GDM should be reasonable and convincible,the criterion of consistency is adopted for judging the compatibility of group judgments.More specifically,the algorithm takes into account not only objective attributes of networks but also the preference of subscribers and traffic class.Hence it guarantees that the subscribers can not select the networks with poor performance depending on their preference.The simulation results show that the proposed algorithm can effectively reduce the handoff number and provide subscribers with satisfactory quality of service(QoS).     

A prescriptive production-distribution approach for decision makingin new product design

The production problem in product design consists of determining technically feasible options and implementing collective decision making to decide which alternatives to produce. A problem with considering collective decision making in the production problem is that it may not be possible to define, a priori, a group utility function, due to the difficulty of making interpersonal comparisons of the members' preferences. Constructing a group utility function and solving the production problem may lead to unacceptable alternatives for some of the members. The distribution problem involves consideration of individual preferences in satisfying group decision-making situations. If production and distribution of the products are considered simultaneously, the preferences of the members are taken into account, explicitly leading to acceptable solutions. This approach also allows for explicit consideration of a company business strategy in determining which products to develop. A production-distribution approach is outlined and demonstrated for the design of a power electronics module product with a group of decision makers consisting of three customers and a manufacturer. In addition to explicit consideration of the customers' preference, some business strategies are also considered, leading to product alternatives acceptable to the customers and manufacturer     

不确定条件下的多Agent自动协商模型

考察了在不确定条件下的双边多议题协商中对Agent的效用建模的问题,给出一个引入了不确定因素的Agent的效用函数形式,更为准确地描述了不确定条件下Agent的偏好.提出了一个不确定条件下的自动协商模型,基于遗传算法设计了协商算法.仿真实验结果表明,该模型能够降低风险,改善Agent在协商过程中的决策效果.     

面向海洋环境的装备选优与排序方法

针对海洋环境下的武器装备选择问题,将其转化为带方案偏好、属性权重完全未知且属性值为区间数形式的多属性决策问题。基于主观偏好与客观属性值偏差最小化的思想,提出一个单目标二次优化模型,并利用LINGO软件进行模型求解,求得属性权重。通过对属性值和属性权重的线性集结,得到排序结果。最后通过数值算例,验证了该方法的可行性和操作性。     

基于QABC-IFMADM算法的有人/无人机编队作战威胁评估

信息化条件下有人/无人机编队作战环境具有高度不确定性,如何对目标进行快速有效的威胁评估成为亟待解决的问题.在直觉模糊集框架下,研究决策者对方案具有偏好,目标属性值为区间数且权重未知的评估问题,提出一种量子蜂群（QABC）优化属性权重的直觉模糊多属性决策（IFMADM）的威胁评估方法.首先,建立方案偏好模型;其次,综合考虑客观优化排序和决策者方案偏好信息,建立主客观最优属性权重优化模型,并采用量子蜂群算法优化属性权重,使得属性权重更准确地反映实际情况.最后,通过编队对地作战威胁评估算例验证了所提方法的合理性与有效性.     

抗电子干扰体系探测资源管控预案设计

针对有多雷达组成的体系级预警装备抗电子干扰难题,开展体系探测资源管控预案设计研究。基于灰靶理论,提出了一种体系探测资源管控预案设计的改进多时段灰靶决策算法。首先,结合灰靶技术和时段偏好模式多样性的特点;然后,针对现有预案设计指标权重主观赋值的随意性引入了客观赋权方法,建立了基于多时段灰靶决策的算法数学模型;最后,通过实例验证所提算法的有效性。实验结果表明,提出的算法具有客观性和可操作性,为设计智能化的预案提供了一种方法和手段。