战场多目标网络重心分析及打击策略生成方法研究
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摘要
现代战争是参战双方作战力量间的高度对抗,对敌目标进行分析,对关键目标实施打击,从而在信息、火力和机动性等领域形成压倒性的对抗优势是军事强国进攻作战的最优先考虑。因此对战场多目标网络特性进行综合分析,总结其中目标遭受打击后的演化规则,探索其重心以及弱点具有十分重要的意义。本文以战场多目标网络打击问题为研究对象,对问题重心进行了分析,并研究了打击策略生成方法。主要包含以下几个方面的内容。
首先,本文对战场多目标网络自身开展了相关的理论研究。详细描述了战场目标、目标影响关联、以及目标演化规则,即备份规则和自修复规则。在图论模型的基础上,构建了较为抽象的战场多目标网络模型,为重心分析和打击策略生成研究提供了基础。
其次,本文提出了基于多实体贝叶斯网络的重心分析框架。该框架利用多实体贝叶斯网络来描述打击对象的不确定性,以及目标演化等不确定信息,在此基础上分析了战场多目标网络中各个目标对网络重心的影响程度。其中,对多实体贝叶斯网络的基本理论以及推理算法进行了分析,并给出了战场多目标网络重心选择方法和实体片断组合算法。
再次,本文提出了基于马尔科夫决策过程的打击策略生成方法。该方法借鉴马尔科夫决策过程在军事计划领域的成功应用,构建了问题抽象后的马尔科夫决策过程模型,包括利用目标的状态构成状态集,利用对目标的打击构成行动集,利用目标状态的变化确定状态转移函数,以及利用之前的重心分析模型构建效益值函数。最后给出了问题的求解算法。
最后,在案例实验中,实现了重心分析的全部过程,使得方法本身的可行性得到了检验,另外还得到了各个目标对网络重心影响的具体数值,并将该数值转化为马尔科夫决策过程模型中的奖励值。在重心分析的基础上,接着给出了案例对应的马尔科夫决策过程模型,通过策略迭代算法,对模型进行了求解,得到了包含在不同状态下开展不同行动的最优打击策略。
战场多目标网络打击问题是军事领域的一个难题,本文在战场多目标网络重心分析的基础上探索了打击策略生成的问题,研究成果可以作为军事指挥人员选择打击目标或生成打击策略时的参考。
The morden warfare could be regarded as the counterwork between the military forces in both sides. To analyze the contrary targets, attack the critical target, and get the superiority in information, firepower, and flexibility are in the first consideration of power nation to carry out the attack operation. So it is meaningful to analyze characteristics of multiple targets network in battlefield, summarize the transformation rules of its elements, and explore its center of gravity (COG) and vulnerability. This paper focused on the attacking problem of multiple targets network in battlefield, analyzed the COG of this multiple targets network, and studied the method of attack strategy generation. This paper contains these works listed below.
Firstly, this paper carried out the theoretical study on multiple targets network in battlefield. It analyzed targets in battlefield, relationships between targets, and transformation rules of targets. Then two transformation rules (backup rule and self-repair rule) were outlined that based on the former analysis. Then, the mode l of multiple targets network in battlefield was built that based on the graph theory model. This model afforded supports for later studies on the COG analysis and the attack strategy generation process.
Secondly, this paper proposed the COG analysis framework, which based on the Multi-Entity Bayesian Networks (MEBNs) theory. This framework used MEBNs to describle the uncertain information such as the uncertainty in target choosing and the evolution of targets, and analyzed the effects of every target on the COG in a multiple targets network. During this study, we introduced the basic theory and inference algorithm of MEBNs, and proposed the COG selection method and the combination algorithm of MFrags.
Thirdly, this paper proposed the method of attack strategy generation, which based on the MDP. As there are so many successful applications that applied MDP in military planning domain, this paper also applied this method in attack strategy generation process of multiple targets network in battlefield. We built the stochastic system of this problem, the state set composed of targets’states, the action set composed of attacks on every target, the transition function based on the transition of targets’states, and value function based on the former results of COG analysis. Then we outlined the algorithm to slove this problem.
Finally, in the case study, we implemented all parts of this COG analysis process, which tested the feasibility of this method. What’s more, we also got the specific impact values of every element on COG from it. These values were transformed into the rewards in Markov Decision Process (MDP), which was the input of attack strategy generation. Then, this MDP mode l of this problem was built that based on the former COG analysis. With the policy-iteration algorithm, we got the optimization attack strategy, which contained different actions refer to different states.
Attacking multiple targets network in battlefield is really a difficult problem in campaign. This paper explored the attack strategy generation problem which based on the COG analysis. The results could afford supports for staff to choose appropriate target or generate optimum attack strategy.
首先,本文对战场多目标网络自身开展了相关的理论研究。详细描述了战场目标、目标影响关联、以及目标演化规则,即备份规则和自修复规则。在图论模型的基础上,构建了较为抽象的战场多目标网络模型,为重心分析和打击策略生成研究提供了基础。
其次,本文提出了基于多实体贝叶斯网络的重心分析框架。该框架利用多实体贝叶斯网络来描述打击对象的不确定性,以及目标演化等不确定信息,在此基础上分析了战场多目标网络中各个目标对网络重心的影响程度。其中,对多实体贝叶斯网络的基本理论以及推理算法进行了分析,并给出了战场多目标网络重心选择方法和实体片断组合算法。
再次,本文提出了基于马尔科夫决策过程的打击策略生成方法。该方法借鉴马尔科夫决策过程在军事计划领域的成功应用,构建了问题抽象后的马尔科夫决策过程模型,包括利用目标的状态构成状态集,利用对目标的打击构成行动集,利用目标状态的变化确定状态转移函数,以及利用之前的重心分析模型构建效益值函数。最后给出了问题的求解算法。
最后,在案例实验中,实现了重心分析的全部过程,使得方法本身的可行性得到了检验,另外还得到了各个目标对网络重心影响的具体数值,并将该数值转化为马尔科夫决策过程模型中的奖励值。在重心分析的基础上,接着给出了案例对应的马尔科夫决策过程模型,通过策略迭代算法,对模型进行了求解,得到了包含在不同状态下开展不同行动的最优打击策略。
战场多目标网络打击问题是军事领域的一个难题,本文在战场多目标网络重心分析的基础上探索了打击策略生成的问题,研究成果可以作为军事指挥人员选择打击目标或生成打击策略时的参考。
The morden warfare could be regarded as the counterwork between the military forces in both sides. To analyze the contrary targets, attack the critical target, and get the superiority in information, firepower, and flexibility are in the first consideration of power nation to carry out the attack operation. So it is meaningful to analyze characteristics of multiple targets network in battlefield, summarize the transformation rules of its elements, and explore its center of gravity (COG) and vulnerability. This paper focused on the attacking problem of multiple targets network in battlefield, analyzed the COG of this multiple targets network, and studied the method of attack strategy generation. This paper contains these works listed below.
Firstly, this paper carried out the theoretical study on multiple targets network in battlefield. It analyzed targets in battlefield, relationships between targets, and transformation rules of targets. Then two transformation rules (backup rule and self-repair rule) were outlined that based on the former analysis. Then, the mode l of multiple targets network in battlefield was built that based on the graph theory model. This model afforded supports for later studies on the COG analysis and the attack strategy generation process.
Secondly, this paper proposed the COG analysis framework, which based on the Multi-Entity Bayesian Networks (MEBNs) theory. This framework used MEBNs to describle the uncertain information such as the uncertainty in target choosing and the evolution of targets, and analyzed the effects of every target on the COG in a multiple targets network. During this study, we introduced the basic theory and inference algorithm of MEBNs, and proposed the COG selection method and the combination algorithm of MFrags.
Thirdly, this paper proposed the method of attack strategy generation, which based on the MDP. As there are so many successful applications that applied MDP in military planning domain, this paper also applied this method in attack strategy generation process of multiple targets network in battlefield. We built the stochastic system of this problem, the state set composed of targets’states, the action set composed of attacks on every target, the transition function based on the transition of targets’states, and value function based on the former results of COG analysis. Then we outlined the algorithm to slove this problem.
Finally, in the case study, we implemented all parts of this COG analysis process, which tested the feasibility of this method. What’s more, we also got the specific impact values of every element on COG from it. These values were transformed into the rewards in Markov Decision Process (MDP), which was the input of attack strategy generation. Then, this MDP mode l of this problem was built that based on the former COG analysis. With the policy-iteration algorithm, we got the optimization attack strategy, which contained different actions refer to different states.
Attacking multiple targets network in battlefield is really a difficult problem in campaign. This paper explored the attack strategy generation problem which based on the COG analysis. The results could afford supports for staff to choose appropriate target or generate optimum attack strategy.
引文
[1] Maier, M.. Architecting Principles for Systems of Systems[C]. Proceedings of the Sixth Annual International Symposium, International Counc il on Systems Engineering, Boston, 1996, 567-574.
[2] Jan L. R., Jonathan P. D.. Centers of Gravity from the“Inside Out”[J]. Joint Force Quarterly, 2011, Issue 60, 1st qurter, 120-125.
[3] Lucia F.. Using Bayesian network analysis to support centre of gravity analysis in military planning[J]. European Journal of Operational Research, 2006, 170, 629-643.
[4] Kuter, U., Nau, D., Gossink, D., Lemmer, J. F.. Interactive course-of-action planning using causal models[C]. In: Proceedings of the Third International Conference on Knowledge Systems for Coalition Operations (KSCO-2004), Czech Republic, 2004, 1-12.
[5] Jouni P.. Decision Analytical Approach to Effects-Based Operations[D]. Master Thesis of HELSINKI UNIVERSITY OF TECHNOLOGY, 2009, 35-63.
[6]秦前付,曹存根,徐洸.基于图论的计划军事效果评估[J].计算机科学, 2005, 32(7), 148-151.
[7]朱延广,朱一帆.基于影响网络的联合火力打击目标选择方法研究[J].军事运筹与系统工程, 2010, 9(24), 64-69.
[8]张最良.军事战略运筹分析方法[M].军事科学出版社, 2009, 233-235.
[9] Albert, R., Jeong H., Barabtsi, A. L.. Attack and Error Tolerance of Complex Networks[J]. Nature, 2000, 406, 378-382.
[10] Carley, K. M.. Dynamic Network Analysis in Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers[J]. Committee on Human Factors, National Research Council, 2003, 133-145.
[11]吴俊,谭跃进.复杂网络抗毁性测度研究[J].系统工程学报, 2005, 20(2), 128-131.
[12]邓宏钟,吴俊,李勇,吕欣,谭跃进.复杂网络拓扑结构对系统抗毁性影响研究[J].系统工程与电子技术, 2008, 30(12), 2425-2428.
[13] S. Rinaldi, J. Peerenboom, T. Kelly. Identifying, Understanding, and Analyzing Critical Infrastructure Interdependencies[J]. IEEE Control Systems Magazine, IEEE, 2001, 11-25.
[14]孙林,胡晓峰,李志强.基于Agent能源管网基础设施模型设计与研究[J].系统仿真学报, 2009, 21(21), 6930-6934.
[15]张川,谢付强,陈云翔,张宣.预警探测体系作战效能评估框架[J].火力与指挥控制, 2008, 12(33), 84-87.
[16] Department of the Army Field Manual FM 3-01.11 Appendix A - ADA Employment Principles, Guidelines, and Priorities[EB/OL]. Accessed at http://www.globalsecurity.org/military/library/policy/army/fm/3-01-11/appa.html, 2003.
[17] D. Koller, A. Pfeffer. Object-oriented bayesian networks[C]. In Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-97), Providence, Rhode Island, 1997, 302-313.
[18] A. Goldberg, D. Robson. Smalltalk-80: The Language and its Implementation[M]. Addison-Wesley, 1983, 15-20.
[19] Martin N., Norman F., Lars N.. Building Large-Scale Bayesian Networks[J]. The Knowledge Engineering Review, 2000.
[20]代京,张平,李行善,于劲松.综合运载器健康管理健康评估技术研究[J].宇航学报, 2009, 30(4), 1711-1721.
[21]代京,张平,李行善,于劲松.航空机电系统测试性建模与分析新方法[J].航空学报, 2010, 31(2), 277-284.
[22] N. Friedman, L. Getoor, D. Koller, A. Pfeffer. Learning probabilistic relational models[C]. In Proceedings of the 16th International Joint Conference on Artificial Intelligence, Stockholm: Morgan Kaufman, 1999, 1300-1309.
[23]陈英武,高妍方.贝叶斯网络扩展研究综述[J].控制与决策, 2008, 23(10), 1081-1086.
[24] Laskey, K. B.. MEBN: A Language for First-Order Bayesian Knowledge Bases[J]. Artificial Intelligence, 2007, 172(23), 1-51.
[25] Laskey, K. B., Mahoney, S. M.. "Network Fragments: Representing Knowledge for Constructing Probabilistic Models."[C]. Uncertainty in Artificial Intelligence: Proceedings of the Thirteenth Conference, San Mateo, CA, Morgan Kaufmann, 1997.
[26] Laskey, K. B., Mahoney, S. M., Wright, E.. "Hypothesis Management in Situation-Specific Network Construction."[C]. Uncertainty in Artificial Intelligence: Proceedings of the Seventeenth Conference, San Mateo, CA, Morgan Kaufman, 2001.
[27] Laskey, K. B., Costa, P.. "Of Klingons and Starships: Bayesian Logic for the 23rd Century."[C]. Uncertainty in Artificial Intelligence: Proceedings of the Twenty-first Conference, Arlington, VA, AUAI Press, 2005.
[28] Wright, E., Mahoney, S. M., Laskey, K. B., Takikawa, M., Levitt, T.. Multi-Entity Bayesian Networks for Situation Assessment[C]. In: Proceedings of theFifth International Conference on Information Fusion, Arlington, 2002, vol. 2, 804-811.
[29] Deng, H. J., Yin, Q. J., Hu, J. W., Cha, Y. B.. Tactical intention recognition based on multi-entity Bayesian network[J]. Systems Engineering and Electronics, 2010, 32(11), 2374-2379.
[30] Laskey, K. B., D'Ambrosio, B., Levitt, T. S., Mahoney, S. M.. "Limited Rationality in Action: Decision Support for Military Situation Assessment."[J]. Minds and Machines, 2000, vol. 10, 53-77.
[31] Alghamdi, G., Laskey, K. B., Wright, E., Barbara, D., Chang, K. C.. "Modeling Insider Behavior Using Multi-Entity Bayesian Networks."[C]. 10th Annual Command and Control Research and Technology Symposium, 2005.
[32] Costa, P.. Bayesian Semantics for the Semantic Web[D]. Doctoral Dissertation, Fairfax, VA: School of Information Technology and Engineering, George Mason University, 2005.
[33] Costa, P. C. G., Laskey, K. B.; Laskey, K. J., Pool, M.. PR-OWL: A Bayesian Framework for the Semantic Web[C]. Proceedings of the First ISWC Workshop on Uncertainty Reasoning for the Semantic Web. Galway, Ireland, 2005.
[34] Costa, P. C. G., Fung, F., Laskey, K. B., Laskey, K. J., Pool, M.. PR-OWL: A framework for probabilistic ontologies[C]. Proceedings of the Second ISWC Workshop on Uncertainty Reasoning for the Semantic Web. Athens, GA, USA, 2006.
[35] Fung, F., Laskey, K.B., Pool, M., Takikawa, M., and Wright, E.. "PLASMA: Combining Predicate Logic and Probability for Information Fusion and Decision Support."[C]. AAAI Spring Symposium on Decision Support in a Changing World, 2005.
[36] Carvalho, R. N., Santos, L. L., Ladeira, M., Costa, P. C. G.. A Tool for Plausible Reasoning in the Semantic Web using MEBN[C]. In Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications, IEEE Press, 2007, 381-386.
[37]姜云飞,杨强,凌应标.自动规划:理论和实践[M].清华大学出版社, 2008, 1-2.
[38] Matlin S.. A Review of the Literature on the Missile Allocation Problem[J]. Operations Research, 1970, 18(3), 334-373.
[39] Patrick A. H., et al. An Asymptotic Result for the Multi-stage Weapon-target Allocation Problem[C]. In the Proceedings of the 29th Conference on Decision and Control, 1990, 240-245.
[40] Koleszar G. E.. A Description of the Weapon Optimization and Resource Requirements Model[R]. IDA, D-2360, 1999.
[41]欧阳才超,李为民,阳曙光.联合火力打击目标分配[J].空军工程大学学报(自然科学版), 2009, 10(3), 50-54.
[42]王振宇,马亚平,李柯.联合火力打击火力分配方案优化方法研究[J].军事运筹与系统工程, 2005, 19(2), 12-17.
[43]蔡怀平,陈英武.武器—目标分配(WTA)问题研究进展[J].火力与指挥控制, 2006, 31(12), 11-15.
[44] Mehdi A., Luca F. B., Jonathan P.. How A Robust Approach to the UAV Task Assignment Problem[C]. Proceedings of the 45th IEEE Conference on Decision & Control, Manchester Grand Hyatt Hotel, San Diego, CA, USA, 2006, 5935-5940.
[45]孟波波,高晓光,丁琳.基于突发任务多无人作战飞机攻击多目标研究[J].系统仿真学报, 2007.01, 19(1), 176-179.
[46] J. S. Bellingham, M. J. Tillerson, A. G. Richards, J. P.. How, Multi-Task Assignment and Path Planning for Cooperating UAVs[C]. Conference on Cooperative Control and Optimization, 2001.
[47]霍霄华.多UCAV动态协同任务规划建模与滚动优化方法研究[D].博士学位论文,国防科学技术大学, 2007, 1-5.
[48] Genshe C., Jose B. C.. Genetic Algorithm for Task Allocation in UAV Cooperative Control[C]. AIAA Conference on Guidance, Navigation, and Control, 2003.
[49]罗泉.多威胁条件下对地攻击行动综合航迹规划与任务分配方法研究[D].硕士学位论文,国防科学技术大学, 2010, 48-56.
[50] Douglas A., Sylvie T., and Lin Z.. Decision-theoretic military operations planning[C]. In Proc. ICAPS, AAAI, 2004, vol. 14, 402-411.
[51] Douglas A.. The factored policy-gradient planner[R]. Technical report, NICTA, 2005.
[52] Abdeslem B.. Planning military operations under uncertainty[R]. Defence Research & Development Canada, Valcartier, 2002, 11-15.
[53] Abdeslem B.. Decision support planners for military operations[R]. Defence Research & Development Canada, Valcartier, 2002, 1-8.
[54] Meirina, C., Yuri N. Levchuk, Georgiy M. Levchuk, Krishna R. Pattipati, and David L. Kleinman. Goal management in organizations: A Markov Decision Process (MDP) approach[C]. 2002 Command and Control Research and Technology Symposium, Naval Postgraduate School, Monterey, California, 2002.
[55] Felipe M., Yuqing T., Katia S., Simon P.. On representing planning domains under uncertainty[J]. ACITA, 2010.
[56]杨萍,毕义明,孙淑玲.具有自主决策能力的机动单元智能体研究[J].兵工学报, 2007, 28(11), 1363-1366.
[57]许全云,张诤敏,周友运.基于马尔柯夫决策过程最优化的动态武器目标分配方法[J].弹箭与制导学报, 2006, 26(2), 1040-1041.
[58]曾伟,周剑岚,王红卫.应急决策的理论与方法探讨[J].中国安全科学学报, 2009, 19(3), 172-176.
[59]胡孝民,应甫成.联合火力战理论研究[M].国防大学出版社, 2005, 31-32.
[60]朱和平. 21世纪预警探测系统[M].军事科学出版社, 2004, 1-2.
[61]雷厉.侦察与监视—作战空间的千里眼和顺风耳[M].国防工业出版社, 2008, 53.
[62]张冬辰.军事通信—信息化战争的神经系统[M].国防工业出版社, 2008, 1-2.
[63] Nils K. S., Stephen D. W.. Connectivity models of interdependency in mixed-type critical infrastructure networks[R]. Information Security Technical Report, 2007, 44-55.
[64]毛子骏,费奇,欧阳敏,洪流.关联基础设施网络模型研究综述[J].计算机科学, 2009, 36(3), 5-8.
[65] Reid E. V. S.. A System-of-Systems (SoS) Approach for Improved Decision Making in Infrastructure Project Selection Problem[D]. Thesis of Doctor of Philosophy, PURDUE UNIVERSITY, 2008.
[66] J. Pearl. Fusion, Propagation, and Structuring in Belief Networks[J]. Artificial Intelligence, 1986, 29(3), 241-288.
[67]高妍方.判别贝叶斯网络的学习算法及其应用研究[D].博士学位论文,国防科学技术大学, 2008, 1-2.
[68] Heckellllan D., Wellman M. P. Bayesian Networks[J]. Communications of the ACM, 1995, 38(3), 27-30.
[69]刘俊娜.贝叶斯网络推理算法[D].硕士学位论文,合肥工业大学, 2007, 18-24.
[70]厉海涛,金光,周经伦,周忠宝,李大庆.贝叶斯网络推理算法综述[J].系统工程与电子技术, 2008, 30(5), 935-939.
[71] Zhou Y., Zhu C., Lei T., Zhang W. M., Liu Z.. A COG Analysis Model of System-of-Systems (SoS) Based on Multi-Entity Bayesian Networks (MEBNs)[C]. The 13th International Conference on Artificial Intelligence (ICAI’11). Las Vegas,Nevada, USA, 2011.
[72] GeNIe 2.0[EB/OL]. Decision Systems Laboratory, University of Pittsburgh, Accessed at http://dsl.sis.pitt.edu/, 2010.
[73] Markov Decision Processes (MDP) Toolbox v3.0 for MATLAB[EB/OL]. Biometry and Artificial Intelligence Unit, INRA Toulouse, Accessed at http://www.toulouse.inra.fr/, 2009.
[2] Jan L. R., Jonathan P. D.. Centers of Gravity from the“Inside Out”[J]. Joint Force Quarterly, 2011, Issue 60, 1st qurter, 120-125.
[3] Lucia F.. Using Bayesian network analysis to support centre of gravity analysis in military planning[J]. European Journal of Operational Research, 2006, 170, 629-643.
[4] Kuter, U., Nau, D., Gossink, D., Lemmer, J. F.. Interactive course-of-action planning using causal models[C]. In: Proceedings of the Third International Conference on Knowledge Systems for Coalition Operations (KSCO-2004), Czech Republic, 2004, 1-12.
[5] Jouni P.. Decision Analytical Approach to Effects-Based Operations[D]. Master Thesis of HELSINKI UNIVERSITY OF TECHNOLOGY, 2009, 35-63.
[6]秦前付,曹存根,徐洸.基于图论的计划军事效果评估[J].计算机科学, 2005, 32(7), 148-151.
[7]朱延广,朱一帆.基于影响网络的联合火力打击目标选择方法研究[J].军事运筹与系统工程, 2010, 9(24), 64-69.
[8]张最良.军事战略运筹分析方法[M].军事科学出版社, 2009, 233-235.
[9] Albert, R., Jeong H., Barabtsi, A. L.. Attack and Error Tolerance of Complex Networks[J]. Nature, 2000, 406, 378-382.
[10] Carley, K. M.. Dynamic Network Analysis in Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers[J]. Committee on Human Factors, National Research Council, 2003, 133-145.
[11]吴俊,谭跃进.复杂网络抗毁性测度研究[J].系统工程学报, 2005, 20(2), 128-131.
[12]邓宏钟,吴俊,李勇,吕欣,谭跃进.复杂网络拓扑结构对系统抗毁性影响研究[J].系统工程与电子技术, 2008, 30(12), 2425-2428.
[13] S. Rinaldi, J. Peerenboom, T. Kelly. Identifying, Understanding, and Analyzing Critical Infrastructure Interdependencies[J]. IEEE Control Systems Magazine, IEEE, 2001, 11-25.
[14]孙林,胡晓峰,李志强.基于Agent能源管网基础设施模型设计与研究[J].系统仿真学报, 2009, 21(21), 6930-6934.
[15]张川,谢付强,陈云翔,张宣.预警探测体系作战效能评估框架[J].火力与指挥控制, 2008, 12(33), 84-87.
[16] Department of the Army Field Manual FM 3-01.11 Appendix A - ADA Employment Principles, Guidelines, and Priorities[EB/OL]. Accessed at http://www.globalsecurity.org/military/library/policy/army/fm/3-01-11/appa.html, 2003.
[17] D. Koller, A. Pfeffer. Object-oriented bayesian networks[C]. In Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-97), Providence, Rhode Island, 1997, 302-313.
[18] A. Goldberg, D. Robson. Smalltalk-80: The Language and its Implementation[M]. Addison-Wesley, 1983, 15-20.
[19] Martin N., Norman F., Lars N.. Building Large-Scale Bayesian Networks[J]. The Knowledge Engineering Review, 2000.
[20]代京,张平,李行善,于劲松.综合运载器健康管理健康评估技术研究[J].宇航学报, 2009, 30(4), 1711-1721.
[21]代京,张平,李行善,于劲松.航空机电系统测试性建模与分析新方法[J].航空学报, 2010, 31(2), 277-284.
[22] N. Friedman, L. Getoor, D. Koller, A. Pfeffer. Learning probabilistic relational models[C]. In Proceedings of the 16th International Joint Conference on Artificial Intelligence, Stockholm: Morgan Kaufman, 1999, 1300-1309.
[23]陈英武,高妍方.贝叶斯网络扩展研究综述[J].控制与决策, 2008, 23(10), 1081-1086.
[24] Laskey, K. B.. MEBN: A Language for First-Order Bayesian Knowledge Bases[J]. Artificial Intelligence, 2007, 172(23), 1-51.
[25] Laskey, K. B., Mahoney, S. M.. "Network Fragments: Representing Knowledge for Constructing Probabilistic Models."[C]. Uncertainty in Artificial Intelligence: Proceedings of the Thirteenth Conference, San Mateo, CA, Morgan Kaufmann, 1997.
[26] Laskey, K. B., Mahoney, S. M., Wright, E.. "Hypothesis Management in Situation-Specific Network Construction."[C]. Uncertainty in Artificial Intelligence: Proceedings of the Seventeenth Conference, San Mateo, CA, Morgan Kaufman, 2001.
[27] Laskey, K. B., Costa, P.. "Of Klingons and Starships: Bayesian Logic for the 23rd Century."[C]. Uncertainty in Artificial Intelligence: Proceedings of the Twenty-first Conference, Arlington, VA, AUAI Press, 2005.
[28] Wright, E., Mahoney, S. M., Laskey, K. B., Takikawa, M., Levitt, T.. Multi-Entity Bayesian Networks for Situation Assessment[C]. In: Proceedings of theFifth International Conference on Information Fusion, Arlington, 2002, vol. 2, 804-811.
[29] Deng, H. J., Yin, Q. J., Hu, J. W., Cha, Y. B.. Tactical intention recognition based on multi-entity Bayesian network[J]. Systems Engineering and Electronics, 2010, 32(11), 2374-2379.
[30] Laskey, K. B., D'Ambrosio, B., Levitt, T. S., Mahoney, S. M.. "Limited Rationality in Action: Decision Support for Military Situation Assessment."[J]. Minds and Machines, 2000, vol. 10, 53-77.
[31] Alghamdi, G., Laskey, K. B., Wright, E., Barbara, D., Chang, K. C.. "Modeling Insider Behavior Using Multi-Entity Bayesian Networks."[C]. 10th Annual Command and Control Research and Technology Symposium, 2005.
[32] Costa, P.. Bayesian Semantics for the Semantic Web[D]. Doctoral Dissertation, Fairfax, VA: School of Information Technology and Engineering, George Mason University, 2005.
[33] Costa, P. C. G., Laskey, K. B.; Laskey, K. J., Pool, M.. PR-OWL: A Bayesian Framework for the Semantic Web[C]. Proceedings of the First ISWC Workshop on Uncertainty Reasoning for the Semantic Web. Galway, Ireland, 2005.
[34] Costa, P. C. G., Fung, F., Laskey, K. B., Laskey, K. J., Pool, M.. PR-OWL: A framework for probabilistic ontologies[C]. Proceedings of the Second ISWC Workshop on Uncertainty Reasoning for the Semantic Web. Athens, GA, USA, 2006.
[35] Fung, F., Laskey, K.B., Pool, M., Takikawa, M., and Wright, E.. "PLASMA: Combining Predicate Logic and Probability for Information Fusion and Decision Support."[C]. AAAI Spring Symposium on Decision Support in a Changing World, 2005.
[36] Carvalho, R. N., Santos, L. L., Ladeira, M., Costa, P. C. G.. A Tool for Plausible Reasoning in the Semantic Web using MEBN[C]. In Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications, IEEE Press, 2007, 381-386.
[37]姜云飞,杨强,凌应标.自动规划:理论和实践[M].清华大学出版社, 2008, 1-2.
[38] Matlin S.. A Review of the Literature on the Missile Allocation Problem[J]. Operations Research, 1970, 18(3), 334-373.
[39] Patrick A. H., et al. An Asymptotic Result for the Multi-stage Weapon-target Allocation Problem[C]. In the Proceedings of the 29th Conference on Decision and Control, 1990, 240-245.
[40] Koleszar G. E.. A Description of the Weapon Optimization and Resource Requirements Model[R]. IDA, D-2360, 1999.
[41]欧阳才超,李为民,阳曙光.联合火力打击目标分配[J].空军工程大学学报(自然科学版), 2009, 10(3), 50-54.
[42]王振宇,马亚平,李柯.联合火力打击火力分配方案优化方法研究[J].军事运筹与系统工程, 2005, 19(2), 12-17.
[43]蔡怀平,陈英武.武器—目标分配(WTA)问题研究进展[J].火力与指挥控制, 2006, 31(12), 11-15.
[44] Mehdi A., Luca F. B., Jonathan P.. How A Robust Approach to the UAV Task Assignment Problem[C]. Proceedings of the 45th IEEE Conference on Decision & Control, Manchester Grand Hyatt Hotel, San Diego, CA, USA, 2006, 5935-5940.
[45]孟波波,高晓光,丁琳.基于突发任务多无人作战飞机攻击多目标研究[J].系统仿真学报, 2007.01, 19(1), 176-179.
[46] J. S. Bellingham, M. J. Tillerson, A. G. Richards, J. P.. How, Multi-Task Assignment and Path Planning for Cooperating UAVs[C]. Conference on Cooperative Control and Optimization, 2001.
[47]霍霄华.多UCAV动态协同任务规划建模与滚动优化方法研究[D].博士学位论文,国防科学技术大学, 2007, 1-5.
[48] Genshe C., Jose B. C.. Genetic Algorithm for Task Allocation in UAV Cooperative Control[C]. AIAA Conference on Guidance, Navigation, and Control, 2003.
[49]罗泉.多威胁条件下对地攻击行动综合航迹规划与任务分配方法研究[D].硕士学位论文,国防科学技术大学, 2010, 48-56.
[50] Douglas A., Sylvie T., and Lin Z.. Decision-theoretic military operations planning[C]. In Proc. ICAPS, AAAI, 2004, vol. 14, 402-411.
[51] Douglas A.. The factored policy-gradient planner[R]. Technical report, NICTA, 2005.
[52] Abdeslem B.. Planning military operations under uncertainty[R]. Defence Research & Development Canada, Valcartier, 2002, 11-15.
[53] Abdeslem B.. Decision support planners for military operations[R]. Defence Research & Development Canada, Valcartier, 2002, 1-8.
[54] Meirina, C., Yuri N. Levchuk, Georgiy M. Levchuk, Krishna R. Pattipati, and David L. Kleinman. Goal management in organizations: A Markov Decision Process (MDP) approach[C]. 2002 Command and Control Research and Technology Symposium, Naval Postgraduate School, Monterey, California, 2002.
[55] Felipe M., Yuqing T., Katia S., Simon P.. On representing planning domains under uncertainty[J]. ACITA, 2010.
[56]杨萍,毕义明,孙淑玲.具有自主决策能力的机动单元智能体研究[J].兵工学报, 2007, 28(11), 1363-1366.
[57]许全云,张诤敏,周友运.基于马尔柯夫决策过程最优化的动态武器目标分配方法[J].弹箭与制导学报, 2006, 26(2), 1040-1041.
[58]曾伟,周剑岚,王红卫.应急决策的理论与方法探讨[J].中国安全科学学报, 2009, 19(3), 172-176.
[59]胡孝民,应甫成.联合火力战理论研究[M].国防大学出版社, 2005, 31-32.
[60]朱和平. 21世纪预警探测系统[M].军事科学出版社, 2004, 1-2.
[61]雷厉.侦察与监视—作战空间的千里眼和顺风耳[M].国防工业出版社, 2008, 53.
[62]张冬辰.军事通信—信息化战争的神经系统[M].国防工业出版社, 2008, 1-2.
[63] Nils K. S., Stephen D. W.. Connectivity models of interdependency in mixed-type critical infrastructure networks[R]. Information Security Technical Report, 2007, 44-55.
[64]毛子骏,费奇,欧阳敏,洪流.关联基础设施网络模型研究综述[J].计算机科学, 2009, 36(3), 5-8.
[65] Reid E. V. S.. A System-of-Systems (SoS) Approach for Improved Decision Making in Infrastructure Project Selection Problem[D]. Thesis of Doctor of Philosophy, PURDUE UNIVERSITY, 2008.
[66] J. Pearl. Fusion, Propagation, and Structuring in Belief Networks[J]. Artificial Intelligence, 1986, 29(3), 241-288.
[67]高妍方.判别贝叶斯网络的学习算法及其应用研究[D].博士学位论文,国防科学技术大学, 2008, 1-2.
[68] Heckellllan D., Wellman M. P. Bayesian Networks[J]. Communications of the ACM, 1995, 38(3), 27-30.
[69]刘俊娜.贝叶斯网络推理算法[D].硕士学位论文,合肥工业大学, 2007, 18-24.
[70]厉海涛,金光,周经伦,周忠宝,李大庆.贝叶斯网络推理算法综述[J].系统工程与电子技术, 2008, 30(5), 935-939.
[71] Zhou Y., Zhu C., Lei T., Zhang W. M., Liu Z.. A COG Analysis Model of System-of-Systems (SoS) Based on Multi-Entity Bayesian Networks (MEBNs)[C]. The 13th International Conference on Artificial Intelligence (ICAI’11). Las Vegas,Nevada, USA, 2011.
[72] GeNIe 2.0[EB/OL]. Decision Systems Laboratory, University of Pittsburgh, Accessed at http://dsl.sis.pitt.edu/, 2010.
[73] Markov Decision Processes (MDP) Toolbox v3.0 for MATLAB[EB/OL]. Biometry and Artificial Intelligence Unit, INRA Toulouse, Accessed at http://www.toulouse.inra.fr/, 2009.