基于改进竞争性假设分析的战役企图分析方法
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摘要
对敌方战役企图的分析判断是确定我方战役企图和制定、实施战役计划的基础,对战役的成败具有至关重要的作用。首先,对战役企图和竞争性假设分析(analysis of competing hypothesis,ACH)进行了介绍。然后,分析了现有ACH存在的不足,并给出了改进方法,即在证据敏感性分析中利用贝叶斯网络描述证据与假设关系,利用理想点解法(technique for order preference by similarity to solution,TOPSIS)对证据进行欺骗性评估,根据证据的欺骗可能性对各种假设进行推理。最后,以诺曼底登陆战役为例,从德军的视角使用所提方法对盟军登陆点进行了分析,证明了改进后的ACH方法可以更加合理、有效地应用于战役企图分析。
The analysis and judgment of enemy campaign attempt are the basis for determining our own campaign attempt and for formulating and implementing our own campaign plan.They are crucial to the success or failure of the campaign.First,concepts of campaign attempt and analysis of competing hypothesis(ACH)are introduced briefly.Then,it analyzes the shortcomings of the existing ACH and gives the improving method.At the step of sensitivity analysis,a Bayesian network is used for describing relation of evidence and hypothesis at first.Then,technique for order preference by similarity to solution(TOPSIS)is used for denial &deception assessment of evidence.At last,Bayesian networks is used to infer possibility of hypothesis basing on denial &deception possibility of evidence.Finally,taking the Normandy Landing Campaign as an example,the method of this article is used to analyze the landing zones of Allied Forces from the perspective of the Germans,which proves that the improved ACH can be applied more reasonably and effectively to the campaign attempt analysis.
The analysis and judgment of enemy campaign attempt are the basis for determining our own campaign attempt and for formulating and implementing our own campaign plan.They are crucial to the success or failure of the campaign.First,concepts of campaign attempt and analysis of competing hypothesis(ACH)are introduced briefly.Then,it analyzes the shortcomings of the existing ACH and gives the improving method.At the step of sensitivity analysis,a Bayesian network is used for describing relation of evidence and hypothesis at first.Then,technique for order preference by similarity to solution(TOPSIS)is used for denial &deception assessment of evidence.At last,Bayesian networks is used to infer possibility of hypothesis basing on denial &deception possibility of evidence.Finally,taking the Normandy Landing Campaign as an example,the method of this article is used to analyze the landing zones of Allied Forces from the perspective of the Germans,which proves that the improved ACH can be applied more reasonably and effectively to the campaign attempt analysis.
引文
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[27]CAWTHORNE N.Fighting them on the beaches:the D-day Landings-June 6,1944[M].London:Arcturus Publishing,2017.
[28]ALLPORT A.Browned off and bloody-minded:the British soldier goes to war,1939-1945[M].New Haven:Yale University Press,2015.
[29]BAXTER C F.The normandy campaign,1944:a selected bibliography[M].Westport:Greenwood Publishing Group,1992.
[30]PENROSE J.The D-day companion:leading historians explore history’s greatest amphibious assault[M].London:Bloomsbury Publishing,2014.
[31]季正明,马晓娟.情报分析案例选[M].北京:金城出版社,2016.JI Z M,MA X J.Case selection of intelligence analysis[M].Beijing:Jincheng Press,2016.
[2]STECH F,ELSSSER C.Midway revisited:detecting deception by analysis of competing hypotheses[J].Military Operations Research,2007,12(4):35-55.
[3]崔华华,林涛.论军事情报分析中竞争性假设分析法的应用[J].情报杂志,2006,25(12):53-55.CUI H H,LIN T.The application of analysis method of competitive hypothsis in military intelligence analysis[J].Journal of Information,2006,25(12):53-55.
[4]HECKMAN K E,STECH F J,THOMAS R K,et al.Countering denial and deception[J].Cyber Denial,Deception and Counter Deception.Springer,Cham,2015:109-126.
[5]史建伟,王肖戎.结构化改进竞争性假设分析法[J].社科纵横,2009,24(6):114-116.SHI J W,WANG X R.Structural improvement competitive hypothesis analysis[J].Social Sciences Review,2009,24(6):114-116.
[6]张晨光,姚泽清.基于竞争性假设分析的主成分分析法在情报分析里的运用[J].军事运筹与系统工程,2014,28(1):25-29.ZHANG C G,YAO Z Q.Application of principal component analysis in intelligence analysis based on competitive hypothesis analysis[J].Military Operations Research and Systems Engineering,2014,28(1):25-29.
[7]周晓帆,施国良.竞争性假设分析法在大型突发性事件分析中的应用研究——以“马航MH370失联事件”为例[J].情报杂志,2015,34(1):27-31.ZHOU X F,SHI G L.Applications research of analysis of competing hypotheses in large-scale emergencies analysis:“lost contact of Malaysia airlines 370”as a chosen case[J].Journal of Intelligence,2015,34(1):27-31.
[8]HECKMAN K E,STECH F J.Cyber counter deception:how to detect denial&deception(D&D)[M].Cyber warfare.Springer International publishing,2015:103-140.
[9]WHALEY B.The one percent solution:costs and benefits of military deception[M].Information Strategy and Warfare.British:Routledge,2014:143-175.
[10]KOTT A,MCENEANEY W M.Adversarial reasoning:computational approaches to reading the opponent’s mind[M].Boca Raton:CRC Press,2006.
[11]BARTON W,AYKROYD S S.Textbook of political-military counterdeception:basic principles&methods[M].Washington D C:National Defense Intelligence College,2007.
[12]HEUER JR R J.Psychology of intelligence Analysis[R].Washington,D.C.:Central Intelligence Agency,1999.
[13]FLEISHER C S,BENSOUSSAN B E.Business and competitive analysis:effective application of new and classic methods[M].United States:FT Press,2015.
[14]HOLMES D E,JAIN L C.Introduction to Bayesian networks[C]∥Proc.of the Innovations in Bayesian networks,2008:1-5.
[15]JENSEN F V.An introduction to Bayesian networks[M].London:UCL press,1996.
[16]SWEET T M.A review of statistical rethinking:a Bayesian course with examples in R and stan[M].Los Angeles,CA:SAGE Publications,2017.
[17]LILLEBORGE M,EIDSVIK J.Efficient designs for Bayesian networks with sub-tree bounds[J].Statistics and Computing,2017,27(2):301-318.
[18]LIU H,ZHOU S,LAM W,et al.A new hybrid method for learning Bayesian networks:Separation and reunion[J].Knowledge-Based Systems,2017,121(4):185-197.
[19]KARIMNEZHAD A,MORADI F.Road accident data analysis using Bayesian networks[J].Transportation Letters,2017,9(1):12-19.
[20]ERTEL W.Introduction to artificial intelligence[M].Berlin:Springer,2018.
[21]KENETT R S.Bayesian networks:theory,applications and sensitivity issues[J].Encyclopedia with Semantic Computing and Robotic Intelligence,2017,1(1):1630014.
[22]BRAINES D,THOMAS A,KAPLAN L,et al.Human-inthe-loop situational understanding via subjective Bayesian networks[C]∥Proc.of the 5th International Workshop on Graph Structures for Knowledge Representation and Reasoning,2017.
[23]HWANG C L,YOON K.Multiple attribute decision making:methods and applications a state-of-the-art survey[M].Berlin:Springer,1981.
[24]NDBAN S,DZITAC S,DZITAC I.Fuzzy topsis:ageneral view[J].Procedia Computer Science,2016,91(1):823-831.
[25]WANG D,LU Y,GAN J.An information security evaluation method based on entropy theory and improved TOPSIS[C]∥Proc.of the 2nd IEEE International Conference on Data Science in Cyberspace,2017:595-600.
[26]MOUSTAFAEV J.Delivering exceptional project results:a practical guide to project selection,scoping,estimation and management[M].Plantation:J.Ross Publishing,2010.
[27]CAWTHORNE N.Fighting them on the beaches:the D-day Landings-June 6,1944[M].London:Arcturus Publishing,2017.
[28]ALLPORT A.Browned off and bloody-minded:the British soldier goes to war,1939-1945[M].New Haven:Yale University Press,2015.
[29]BAXTER C F.The normandy campaign,1944:a selected bibliography[M].Westport:Greenwood Publishing Group,1992.
[30]PENROSE J.The D-day companion:leading historians explore history’s greatest amphibious assault[M].London:Bloomsbury Publishing,2014.
[31]季正明,马晓娟.情报分析案例选[M].北京:金城出版社,2016.JI Z M,MA X J.Case selection of intelligence analysis[M].Beijing:Jincheng Press,2016.