针对现实复杂冲突中决策者策略选择偏好不确定性和强度并存的情形,融合概率偏好与强度偏好两种表示方式的优势,提出了基于概率强度偏好的冲突分析图模型(graph model for conflict resolution, GMCR)方法。首先,概述了经典GMCR方法的基...针对现实复杂冲突中决策者策略选择偏好不确定性和强度并存的情形,融合概率偏好与强度偏好两种表示方式的优势,提出了基于概率强度偏好的冲突分析图模型(graph model for conflict resolution, GMCR)方法。首先,概述了经典GMCR方法的基本概念和流程;其次,提出了概率强度偏好结构,以综合表征决策者的偏好情况;在此基础上,重点定义了8种稳定性类型以揭示复杂博弈行为的内在逻辑规则;最后,示例研究了各方策略选择偏好和见招拆招的策略交互过程,验证了以所提方法解决多方冲突的可行性与有效性。展开更多
联合作战背景下的指控流程(command and control process,CCP)涉及同层级内不同指控单元之间的横向信息交互与不同层级间的纵向信息交互。针对这种复杂化与多元化指控流程,研究了如何利用ExtendSim仿真工具对指控流程进行模型构建、验...联合作战背景下的指控流程(command and control process,CCP)涉及同层级内不同指控单元之间的横向信息交互与不同层级间的纵向信息交互。针对这种复杂化与多元化指控流程,研究了如何利用ExtendSim仿真工具对指控流程进行模型构建、验证、评估与优化。首先,通过对指控流程特点进行分析,抽取通用的要素类型,包括实体和关系要素,并映射到ExtendSim关键模块;其次,构建“指控流程要素-ExtendSim模块”的转换规则,提出构建ExtendSim指控流程模型的方法步骤;然后,研究基于ExtendSim的指控流程可行性验证方法(行为一致性、可执行性、合理性验证),提出了基于ExtendSim的指控流程评估指标(任务平均耗时、任务平均等待时间与任务最大处理容量),并研究了基于ExtendSim的指控流程优化方法;最后,通过一个通用反导指控流程案例,验证了所提建模与分析方法的可行性与有效性,可以为指控流程的建模与分析提供支撑和参考。展开更多
Link prediction of combat networks is of significant military value for precisely identifying the vital infrastructure of the enemy target and optimizing the operational plan of our side.Due to the profound uncertaint...Link prediction of combat networks is of significant military value for precisely identifying the vital infrastructure of the enemy target and optimizing the operational plan of our side.Due to the profound uncertainty in the battleground circumstances, the acquired topological information of the opponent combat network always presents sparse characteristics. To solve this problem, a novel approach named network embedding based combat network link prediction(NECLP) is put forward to predict missing links of sparse combat networks. First,node embedding techniques are presented to preserve as much information of the combat network as possible using a low-dimensional space. Then, we put forward a solution algorithm to predict links between combat networks based on node embedding similarity. Last, massive experiments are carried out on a real-world combat network case to verify the validity and practicality of the proposed NECLP. This paper compares six baseline methods, and experimental results show that the NECLP has outstanding performance and substantially outperforms the baseline methods.展开更多
文摘针对现实复杂冲突中决策者策略选择偏好不确定性和强度并存的情形,融合概率偏好与强度偏好两种表示方式的优势,提出了基于概率强度偏好的冲突分析图模型(graph model for conflict resolution, GMCR)方法。首先,概述了经典GMCR方法的基本概念和流程;其次,提出了概率强度偏好结构,以综合表征决策者的偏好情况;在此基础上,重点定义了8种稳定性类型以揭示复杂博弈行为的内在逻辑规则;最后,示例研究了各方策略选择偏好和见招拆招的策略交互过程,验证了以所提方法解决多方冲突的可行性与有效性。
文摘联合作战背景下的指控流程(command and control process,CCP)涉及同层级内不同指控单元之间的横向信息交互与不同层级间的纵向信息交互。针对这种复杂化与多元化指控流程,研究了如何利用ExtendSim仿真工具对指控流程进行模型构建、验证、评估与优化。首先,通过对指控流程特点进行分析,抽取通用的要素类型,包括实体和关系要素,并映射到ExtendSim关键模块;其次,构建“指控流程要素-ExtendSim模块”的转换规则,提出构建ExtendSim指控流程模型的方法步骤;然后,研究基于ExtendSim的指控流程可行性验证方法(行为一致性、可执行性、合理性验证),提出了基于ExtendSim的指控流程评估指标(任务平均耗时、任务平均等待时间与任务最大处理容量),并研究了基于ExtendSim的指控流程优化方法;最后,通过一个通用反导指控流程案例,验证了所提建模与分析方法的可行性与有效性,可以为指控流程的建模与分析提供支撑和参考。
基金supported by the National Natural Science Foundation of China (7190121271971213)。
文摘Link prediction of combat networks is of significant military value for precisely identifying the vital infrastructure of the enemy target and optimizing the operational plan of our side.Due to the profound uncertainty in the battleground circumstances, the acquired topological information of the opponent combat network always presents sparse characteristics. To solve this problem, a novel approach named network embedding based combat network link prediction(NECLP) is put forward to predict missing links of sparse combat networks. First,node embedding techniques are presented to preserve as much information of the combat network as possible using a low-dimensional space. Then, we put forward a solution algorithm to predict links between combat networks based on node embedding similarity. Last, massive experiments are carried out on a real-world combat network case to verify the validity and practicality of the proposed NECLP. This paper compares six baseline methods, and experimental results show that the NECLP has outstanding performance and substantially outperforms the baseline methods.
