基于RTS视角的指挥控制系统智能化技术

doi:10.16182/j.issn1004731x.joss.201811014

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (11): 4158-4171.doi: 10.16182/j.issn1004731x.joss.201811014

基于RTS视角的指挥控制系统智能化技术

伍文峰^1,2, 张昱¹, 荣明¹

1. 国防大学联合作战学院联合作战演训中心,北京 100091;
2. 陆军炮兵防空兵学院军政基础系军事运筹教研室,安徽合肥 230031

收稿日期:2018-05-31 修回日期:2018-06-26 发布日期:2019-01-04
作者简介:伍文峰(1982-),男,湖北钟祥,博士,讲师,研究方向为体系评估、体系建模与仿真;张昱(1979-),男,四川南充,博士,讲师,研究方向为战争复杂系统建模与仿真、智能化指挥与控制。
基金资助:
中国博士后科学基金(2017M613318)

Intelligent Technology of Command and Control System in the RTS Perspective

Wu Wenfeng^1,2, Zhang Yu¹, Rong Ming¹

1. Joint Operations College Joint Operations Training Center, National Defense University, Beijing 100091, China;
2. Military Operations Teaching and Research Office, Army Artillery and Air Defense Academy, Hefei 230031, China

Received:2018-05-31 Revised:2018-06-26 Published:2019-01-04

摘要/Abstract

摘要： 即时战略(Real-Time Strategy,RTS)游戏对于研究指挥控制系统智能化技术有重要参考价值。针对指挥决策过程阐述了RTS游戏与战略战役级指挥控制系统的相似性,分析了RTS游戏中智能化技术面临的规划、学习、不确定和时空推理等问题带来的挑战,研究了行动序列规划、敌方规划识别、状态评估、多智能体协作和多尺度AI等关键技术及最新研究进展,指出了战略战役级指挥控制系统智能化技术发展的趋势,为指挥控制系统的智能化技术开发和研究提供了有益借鉴。

关键词: 即时战略游戏, 指挥控制系统, 人工智能, 任务规划, 深度学习

Abstract: Real-Time Strategy (RTS) games have important reference value for studying the intelligent technology of command and control systems. The similarities between RTS games and the strategic battle level command and control systems are described according to the decision process. The challenges brought by the problems of planning, learning, uncertainty and space-time reasoning in the intelligent technology of RTS games are analyzed. The key technologies and latest research progress of action sequence planning, plan recognition, state assessment, multi-agent collaboration and multi-scale AI are studied. The trend of intelligent technology development of strategic and operational level command and control systems is pointed out. It provides a useful reference for the development and research of intelligent technology for command and control systems.

Key words: Real Time Strategy, Command and Control System, Artificial Intelligence, Task Planning, Deep Learning

中图分类号:

TP391.9

伍文峰, 张昱, 荣明. 基于RTS视角的指挥控制系统智能化技术[J]. 系统仿真学报, 2018, 30(11): 4158-4171.

Wu Wenfeng, Zhang Yu, Rong Ming. Intelligent Technology of Command and Control System in the RTS Perspective[J]. Journal of System Simulation, 2018, 30(11): 4158-4171.

参考文献

[1] 胡晓峰, 郭圣明, 贺筱媛. 指挥信息系统的智能化挑战—“深绿”计划及AlphaGo带来的启示与思考[J]. 军用信息系统与技术, 2016, 7(3): 1-7.
Hu Xiaofeng, Guo Shengming, He Xiaoyuan.Challenge to Intelligent Command Information System: Reason and Revelation on Deep Green Plan and AlphaGo[J]. Command Information System and Technology, 2016, 7(3): 1-7.
[2] Ontañón S, Synnaeve G, Uriarte A, et al.A Survey of Real-Time Strategy Game AI Research and Competition in Starcraft[J]. IEEE Transactions on Computational Intelligence and AI in Games (S1943-068X), 2013, 5(4): 293-311.
[3] Glen Robertson, Ian Watson.A Review of Real-Time Strategy Game AI[J]. AI MAGAZINE(S0738-4602), 2014, 35(4): 75-104.
[4] Peng Peng, Ying Wen, Yaodong Yang, et al. Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-Level Coordination in Learning to Play Starcraft Combat Games[DB/OL]. (2017-03-29) [2018-05-31]. http://cn.arxiv.org/pdf/1703.10069v4.
[5] Nicholas Ernest, David Carroll, Corey Schumacher, et al.Genetic Fuzzy Based Artificial Intelligence for Unmanned Combat Aerial Vehicle Control in Simulated Air Combat Missions[J]. Journal of Defense Management (S2167-0374), 2016, 6(1): 1-7.
[6] Vinyals O, Ewalds T, Bartunov S, et al. Starcraft II: A New Challenge for Reinforcement Learning[DB/OL]. (2017-08-16) [2018-05-31]. http://cn.arxiv.org/pdf/1708. 04782v1.
[7] Palma R, Sánchezruiz A A, Gómezmartín M A, et al.Combining Expert Knowledge and Learning From Demonstration in Real-Time Strategy Games[C]// Case-Based Reasoning Research and Development, International Conference on Case-Based Reasoning, ICCBR 2011, London, UK, September 12-15, 2011. Proceedings. 2011: 181-195.
[8] Weber B G, Ontañón S.Using Automated Replay Annotation for Case-Based Planning in Games[C]// ICCBR 2010 workshop on CBR for Computer Games (ICCBR 2010), 2010: 15-24.
[9] Jaidee U, Aha D W.Integrated Learning for Goal-Driven Autonomy [C]// International Joint Conference on Artificial Intelligence. AAAI Press, 2011: 2450-2455.
[10] Ponsen M J V, Spronck P, Aha D W. Automatically Acquiring Domain Knowledge for Adaptive Game AI Using Evolutionary Learning[C]// Conference on Innovative Applications of Artificial Intelligence. AAAI Press, 2005: 1535-1540.
[11] Wintermute S, Xu J Z, Laird J E.SORTS: A Human-Level Approach to Real-Time Strategy AI[C]// Artificial Intelligence and Interactive Digital Entertainment Conference, June 6-8, 2007, Stanford, California, USA. DBLP, 2008: 55-60.
[12] Perkins L.Terrain Analysis in Real-Time Strategy Games: An Integrated Approach to Choke Point Detection and Region Decomposition[C]// AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, Aiide 2010, October 11-13, 2010, Stanford, California, USA. DBLP, 2010: 223-231.
[13] Yuandong Tian, Qucheng Gong, Wenling Shang, et al.ELF: An Extensive, Lightweight and Flexible Research Platform for Real-Time Strategy Games[C]// 31st Conference and Workshop on Neural Information Processing Systems (NIPS 2017), California, USA, December 4-9, 2017: 2656-2666.
[14] Huikai Wu, Junge Zhang, Kaiqi Huang. MSC: A Dataset for Macro-Management in Starcraft II [DB/OL]. (2017-10-09)[2018-05-31]. http://cn.arxiv.org/pdf/1710. 03131v1.
[15] Weber B G, Mawhorter P, Mateas M, et al.Reactive Planning Idioms for Multi-Scale Game AI[C]// Computational Intelligence and Games. IEEE, 2010: 115-122.
[16] Synnaeve G, Bessiere P.A Bayesian Model for RTS Units Control Applied to Starcraft[C]// Computational Intelligence and Games. IEEE, 2011: 190-196.
[17] Dereszynski E, Hostetler J, Fern A, et al.Learning Probabilistic Behavior Models in Real-Time Strategy Games[C]// AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. AAAI Press, 2011: 20-25.
[18] Mehta M, Ontañón S, Amundsen T, et al.Authoring Behaviors for Games Using Learning From Demonstration[C]// Proc. of the 8th Intenational Conf. on Case-Based Reasoning: Case-Based Reasoning Research and Development (ICCBR 2009). Berlin, Heidelberg: Springer-Verlag, 2009: 12-20.
[19] Kun Shao, Yuanheng Zhu, Dongbin Zhao.Starcraft Micromanagement With Reinforcement Learning and Curriculum Transfer Learning[J]. IEEE Transactions on Emerging Topics in Computational Intelligence (S2471-285X), 2018 (99): 1-12.
[20] Churchill D N, Saffidine A, Buro M, et al.Fast Heuristic Search for RTS Game Combat Scenarios[C]// National Conference on Artificial Intelligence, 2012: 112-117.
[21] Niels Justesen, Sebastian Risi.Learning Macromanagement in Starcraft From Replays Using Deep Learning[C]// IEEE’s 2017 Conference on Computational Intelligence in Games, August 22-25, New York, USA. 2017.
[22] Will Knight. The US Military Is Funding An Effort to Catch Deep Fakes and Other AI Trickery[EB/OL] (2018-05-23) [2018-05-31]. https://www.technologyreview. com/s/611146/the-us-military-is-funding-an-effort-to-catch-deepfakes-and-other-ai-trickery/.

基于RTS视角的指挥控制系统智能化技术

Intelligent Technology of Command and Control System in the RTS Perspective

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