任务规划中基于案例推理的高维解空间适应性问题研究
The Research of High Dimensional Solution Space Adaptation Based on Case-Based Reasoning during Mission Planning
DOI: 10.12677/CSA.2015.512057, PDF,    国家自然科学基金支持
作者: 张媛, 齐玉东, 乔勇军, 陈青华：海军航空工程学院兵器科学与技术系，山东 烟台
关键词: CBR；多维解空间；适应；ViSOM；任务规划；CBR； Multi-Dimensional Solution Space； Adaptation； ViSOM； Mission Planning
摘要: 利用案例推理对指挥实体任务规划过程中决策问题求解方法的修正过程是该方法推理过程中最困难的阶段，尤其当决策问题解空间是多维的情况下。文章讨论了指挥实体任务规划过程中高维决策空间的修正问题，并提出了可行的解决方法。首先利用自组织匹配法(ViSOM)清晰展现问题空间与决策空间的映射过程，然后，利用BP神经网络分析匹配结果间的相关性问题，最后选取一个简化的军事剧情对该方法的合理性进行验证。
Abstract: Adaptation is the most difficult stage in the CBR cycle, especially, when the solution space is multi- dimensional in the Command Entity’s Mission Planning. This paper discusses the adaptation of a high dimensional solution space in the Command Entity’s Mission Planning and proposes a possible approach to it. A Visualization induced Self Organizing Map (ViSOM) is used to map the problem space and solution space first, then a Back Propagation (BP) network is applied to analyze the relations between these two maps. A simple military scenario is used as a case study for evaluation purposes.
文章引用：张媛, 齐玉东, 乔勇军, 陈青华. 任务规划中基于案例推理的高维解空间适应性问题研究[J]. 计算机科学与应用, 2015, 5(12): 454-463. https://dx.doi.org/10.12677/CSA.2015.512057

参考文献

[1] Leake, B., Kinley, A. and Wilson, D. (1996) Acquiring Case Adaptation Knowledge: A Hybrid Approach. Proceedings of the Thirteenth National Conference on Artificial Intelligence, AAAI Press, Menlo Park.
[2] Hanney, K. and Keane, M.T. (1997) The Adaptation Knowledge: How to Easy It by Learning from Cases. Proceedings of the Second International Conference on Case-Based Reasoning, Springer, Berlin, 359-370.
[3] Jarmulark, J., Craw, S. and Rowe, R. (2001) Using Case-Base Data to Learn Adaptation Knowledge for Design. Proceedings of the Seventeenth IJCAI Conference, Morgan Kaufmann, San Mateo, 1011-1016.
[4] Wilke, W., Vollrath, I., Althoff, K.D. and Bergmann, R. (1997) A Framework for Learning Adaptation Knowledge Based on Knowledge Light Approaches, Proceedings of the Fifth German Workshop on Case-Based Reasoning.
[5] 段美美, 于本海, 朱荫. 基于CBR的软件项目成本估算方法[J]. 计算机工程与设计, 2014, 35(11): 3837-3844.
[6] 李蕾, 祁慧敏, 杨凤霞. 基于案例与模糊推理的中医诊断系统研究[J]. 信阳师范学院学报(自然科学版), 2014, 27(4): 585-588.
[7] 董磊, 任章, 李清东. 基于模型和案例推理的混合故障诊断方法[J]. 系统工程与电子技术, 2012, 34(11): 2339-2343.
[8] 任章, 李清东, 董磊, 等. 基于案例推理和等价空间的定性/定量混合诊断故障方法[J]. 南京航空航天大学学报, 2011, 43(5): 87-90.
[9] Bauer, H.U. and Pawelzik, K.R. (1992) Quantifying the Neighbourhood Preservation of Self-Organizing Feature Maps. IEEE Transactions on Neural Networks, 13, 570-579. http://dx.doi.org/10.1109/72.143371 [Google Scholar] [CrossRef] [PubMed]
[10] Kohonen, T. (2001) Self-Organizing Maps. Vol. 30 of Springer Series in Information Sciences, 3rd Edition, Springer- Verlag, Berlin Heidelberg. http://dx.doi.org/10.1007/978-3-642-56927-2 [Google Scholar] [CrossRef] 
[11] Kirk, J.S. and Zurada, J.M. (2000) A Two-Stage Algorithm for Im-proved Topography Preservation n self-Organizing Maps. IEEE International Conference on Systems, Man, and Cy-bernetics, IEEE Service Center, 4, 2527-2532. http://dx.doi.org/10.1109/icsmc.2000.884373 [Google Scholar] [CrossRef] 
[12] Su, M.C. and Chang, H.T. (2001) A New Model of Self-Organizing Neual Networks and Its Application in Data Projection. IEEE Transactions on Neural Networks, 12, 153-158. http://dx.doi.org/10.1109/72.896805 [Google Scholar] [CrossRef] [PubMed]
[13] Pratt, D.R. (2001) Case Based Reasoning for the Next Generation Synthetic Force. Technical Report SAIC-01/7836&00, Science Applications International Corporation, Orlando.