基于妥协案例推理的作战仿真实验范围迁移

doi:10.16182/j.issn1004731x.joss.21-0040

摘要/Abstract

摘要：

作战仿真实验范围通常依靠专家确定，耗费代价较大。为将作战仿真实验的历史数据中关于实验范围选择的知识迁移至新的作战仿真实验案例，提出一种基于妥协策略的案例推理方法。根据案例的数据特点，提出作战仿真实验案例的表示方法；根据案例的结构和属性特点，提出主客观综合赋权的混合相似度计算方法；针对案例检索时出现检索失败和检索结果包含信息量较少的问题，提出基于妥协策略的案例检索方法；根据检索结果的特点，提出实验范围的整合迁移方法。基于历史数据进行了实验分析，证明了方法的有效性。

关键词: 作战仿真实验, 案例推理, 妥协策略, 案例知识迁移

Abstract:

The scope of operational simulation experiment is usually determined by experts, which costs relatively high. In order to transfer the knowledge of experimental scope selection from historical data of operational simulation experiment to new operational experiment cases, the method of compromised case-based reasoning is proposed. According to the data characteristics of the case, the representation method of the operational simulation experiment case is proposed; according to the structure and attribute characteristics of the case, the hybrid similarity calculation method of subjective and objective comprehensive weighting is proposed; aiming at the problems of retrieval failure and less information content in the retrieval results, the case retrieval method based on compromise strategy is proposed;according to the characteristics of the retrieval results, the method of integrating and transferring retrieval results is proposed. The effectiveness of the method is proved by experimental analysis based on historical data.

Key words: operational simulation experiments, CBR(case-Based reasoning), compromise strategy, case knowledge transfer

中图分类号:

E917

马骏, 杨镜宇, 吴曦. 基于妥协案例推理的作战仿真实验范围迁移[J]. 系统仿真学报, 2022, 34(7): 1568-1581.

Jun Ma, Jingyu Yang, Xi Wu. Transfer Method of Operational Simulation Experiment Scope Using Compromised Case-Based Reasoning[J]. Journal of System Simulation, 2022, 34(7): 1568-1581.

图/表 9

图1

图2

图3

图4

图5

表1

表2

表3

表4

参考文献 25

1	别晓峰, 董艳, 王剑飞. 作战仿真实验设计的基本问题探讨[J]. 军事运筹与系统工程, 2008, 22(4): 51-56.
	Bie Xiaofeng, Dong Yan, Wang Jianfei. Discussion on Basic Problems of Operational Simulation Experiment Design[J]. Military Operations Research and Systems Engineering, 2008, 22(4): 51-56.
2	Munoz-Avica H, Aka D W, Nau D S, et al. SiN: Integrating Case-based Reasoning with Task Decomposition[C]// Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-2001). Washington, USA: Morgan kaufmann Publishers Inc, 2001.
3	Aamodt A, Plaza E. Case-based Reasoning: Foundational Issues, Methodological Variations, and System Approaches[J]. AI Communications (S0921-7126), 1994, 7(1): 39-59
4	张佰尚, 李向阳, 王铁男, 等. 集成案例推理综述[J]. 计算机应用研究, 2016, 33(3): 641-647.
	Zhang Baishang, Li Xiangyang, Wang Tienan, et al. Survey on Integrated Case-based Reasoning[J]. Application Research of Computers, 2016, 33(3): 641-647.
5	Lin Shihwei, Chen Shihchieh. Parameter Tuning,Feature Selection and Weight Assignment of Features for Case-Based Reasoning by Artificial Immune System[J]. Applied Soft Computing (S1568-4946), 2011, 11(8): 5042-5052.
6	Ahn H, Kim K J. Bankruptcy Prediction Modeling with Hybrid Case-based Reasoning and Genetic Algorithms Approach[J]. Applied Soft Computing (S1568-4946), 2009, 9(2): 599-607.
7	Teodorovi D, elmi M, Mijatovi-Teodorovi L, et al. Combining Case-based Reasoning with Bee Colony Optimization for dose Planning in Well Differentiated Thyroid Cancer Treatment[J]. Expert Systems with Applications (S0957-4174), 2013, 40(6): 2147-2155.
8	Petrovic S, Mishra N, Sundar S. A Novel Case Based Reasoning Approach to Radiotherapy Planning[J]. Expert Systems with Applications (S0957-4174), 2011, 38(9): 10759-10769.
9	Im K H, Park S C. Case-based Reasoning and Neural Network Based Expert System for Personalization[J]. Expert Systems with Applications (S0957-4174), 2007, 32(1): 77-85.
10	Liao Z L, Mao X W, Hannam P M, et al. Adaptation Methodology of CBR for Environmental Emergency Preparedness System based on an Improved Genetic Algorithm[J]. Expert Systems with Applications (S0957-4174), 2012, 39(8): 7029-7040.
11	Craw S, Wiratunga N, Rowe R C. Learning Adaptation Knowledge to Improve Case-based Reasoning[J]. Artificial Intelligence (S0004-3702), 2006, 170(16/17): 1175-1192.
12	YAN A, YU H, WANG D.Case-based Reasoning Classifier Based on Learning Pseudo Metric Retrieval[J].Expert Systems with Applications (S6957-4174), 2017,89: 91-98.
13	余肖生, 宋锦, 任明霞, 等. 基于伪度量的案例推理改进算法[J]. 计算机技术与发展, 2020, 3(10): 69-74.
	Yu Xiaosheng, Song Jin, Ren Mingxia, et al. Improved Case Reasoning Algorithm Based on Pseudo-Metric[J]. Computer technology and development, 2020, 3(10):69-74.
14	周萍. 我国案例检索研究综述[J]. 情报探索, 2017(1):129-134.
	Zhou Ping. Review of Researches on Case Retrieval in China[J].Information Research, 2017(1): 129-134.
15	张春晓. 案例推理的认知改进策略及学习性能研究[D]. 北京: 北京工业大学, 2014.
	Zhang Chunxiao. Research On The Congnitive Improvement Strategies And Learning Ability For Case-Based Reasoning[D]. Beijing:Beijing University of technology, 2014.
16	杨红美. 基于案例推理的学习迁移研究[D]. 扬州: 扬州大学, 2012.
	Yang Hongmei. Research on Learning Transfer Based on Case Reasoning[D]. Yangzhou:Yangzhou University, 2012.
17	Gebhardt Friedrich. Survey On Structure-Based Case Retrieval[J]. The Knowledge Engineering Reviews(S0269-8889), 1997, 12(1): 41-58.
18	Mantaras R L D, Plaza E. Case-based Reasoning: An Overview[J]. AI Communication (S0921-7126), 1997, 10(1): 21-29.
19	Cover T M, Hart P E. Nearest Neighbor Pattern ClassiﬁCation[J]. IEEE Transactions on Information Theory(S0018-9448), 1967, 13(1): 21-27
20	Liao T W, Zhang Z M, Mount C R. Similarity Measures for Retrieval in Case-based Reasoning systems[J]. Applied Artificial Intelligence (S0883-9514), 1998, 12(4): 267-288.
21	Slonim T Y, Schneider M. Design Issues in Fuzzy Case-Based Reasoning[J]. Fuzzy Sets and Systems (S0165-0114), 2001, 117(2): 251-267.
22	周凯波, 冯珊, 李锋. 基于案例属性特征的相似度计算模型研究[J]. 武汉理工大学学报(信息与管理工程版),2003, 25(1): 24-27.
	Zhou Kaibo, Feng Shan, Li Feng. Research of the Similarity Calculation Models Based on the Features of Case Properties[J]. Journal of WUT(Information & Management Engineering ), 2003, 25(1): 24-27.
23	秦玉平, 杨兴凯. 基于案例推理的区间属性相似度研究[J]. 辽宁师范大学学报, 2006, 29(4): 442-445.
	Qin Yuping, Yang Xingkai. Study of Range Attributes Similarity of Case-based Reasoning[J]. Journal of Liaoning Normal University, 2006, 29(4): 442-445.
24	McSherry David. Similarity and Compromise[M]. Springer Berlin Heidelberg:Case-Based Reasoning Research and Development, 2003: 291-305.
25	Burkhard, Dieter Hans. Extending Some Concepts of CBR-Foundations of Case Retrieval Nets[M]. Springer Berlin Heidelberg:Case-Based Reasoning Technology, 1998: 17-50.

序号	属性名称	属性类型	个数
1	作战区域	连续数值型	4
2	作战样式	枚举型	1
3	红方作战实体总数	离散数值型	1
4	红方作战实体类型总数	离散数值型	1
5	蓝方作战实体总数	离散数值型	1
6	蓝方作战实体类型总数	离散数值型	1
7	实体类型	枚举型	1·e
8	实体指控关系	离散数值型	2·e
9	实体通信关系	离散数值型	2·e
10	实体部署位置	连续数值型	3·e
11	实体行动对象	离散数值型	1·e
12	实验因素范围	区间型	1
13	实验结果数据 (命中情况)	连续数值型	1

属性序号	权重	属性序号	权重
1	0.096 9	8	0.092 5
2	0.070 5	9	0.092 5
3	0.044 1	10	0.110 9
4	0.053 0	11	0.093 2
5	0.035 4	12	0.076 1
6	0.044 3	13	0.084 3
7	0.106 3

实验方案	相似案例个数	检索准确性
1	5.666 7±0.942 8	0.433 3±1.247 2
2	8.333 3±0.471 4	0.633 3±1.247 2
3	9.666 7±0.471 4	0.766 7±0.471 4

实验方案		属性信息量	案例信息量	方案信息量
方案1	测试案例1	0.999 96	0.999 533	0.999 753
	测试案例1	0.999 11	0.999 533
	测试案例2	1.000 00	1.000 000
	测试案例2	1.000 00	1.000 000
	测试案例3	0.999 97	0.999 725
	测试案例3	0.999 48	0.999 725
方案2	测试案例1	0.999 93	0.998 431	0.997 802
	测试案例1	0.996 93	0.998 431
	测试案例2	0.999 91	0.995 317
	测试案例2	0.990 72	0.995 317
	测试案例3	0.999 97	0.999 657
	测试案例3	0.999 34	0.999 657
方案3	测试案例1	0.999 85	0.995 159	0.995 791
	测试案例1	0.990 47	0.995 159
	测试案例2	0.999 89	0.995 276
	测试案例2	0.990 66	0.995 276
	测试案例3	0.999 73	0.996 936
	测试案例3	0.994 15	0.996 936

[1]	宋佩华, 贾金原. 基于案例推理和协同过滤的室内场景生成算法[J]. 系统仿真学报, 2019, 31(2): 263-274.
[2]	宋佩华, 贾金原. 基于案例推理及距离场的家居在线布局方法[J]. 系统仿真学报, 2016, 28(10): 2438-2447.
[3]	谭亚新, 范锐, 肖栋. 基于作战仿真案例推理的陆军装备保障需求测算方法研究[J]. 系统仿真学报, 2015, 27(9): 2056-2065.