兵棋推演的智能决策技术与挑战

尹奇跃; 赵美静; 倪晚成; 张俊格; 黄凯奇

doi:10.16383/j.aas.c210547

兵棋推演的智能决策技术与挑战

doi: 10.16383/j.aas.c210547

尹奇跃^{1, 2,},
赵美静^1,,
倪晚成^{1, 2,},
张俊格^{1, 2,},
黄凯奇^{1, 2,}

1.
中国科学院自动化研究所北京 100190
2.
中国科学院大学北京 100049

基金项目: 国家自然科学青年基金(61906197)资助

详细信息

作者简介:
尹奇跃：中国科学院自动化研究所副研究员. 主要研究方向为强化学习, 数据挖掘和人工智能与游戏. E-mail: qyyin@nlpr.ia.ac.cn

赵美静：中国科学院自动化研究所副研究员. 主要研究方向为知识表示与建模, 复杂系统决策. E-mail: meijing.zhao@ia.ac.cn

倪晚成：中国科学院自动化研究所研究员. 主要研究方向为数据挖掘与知识发现, 复杂系统建模和群体智能博弈决策平台与评估. E-mail: wancheng.ni@ia.ac.cn

张俊格：中国科学院自动化研究所研究员. 主要研究方向为持续学习, 小样本学习, 博弈决策和强化学习. E-mail: jgzhang@nlpr.ia.ac.cn

黄凯奇：中国科学院自动化研究所研究员. 主要研究方向为计算机视觉, 模式识别和认知决策. 本文通信作者. E-mail: kqhuang@nlpr.ia.ac.cn

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出版历程
- 收稿日期: 2021-06-17
- 录用日期: 2021-09-17
- 网络出版日期: 2021-10-24
- 刊出日期: 2023-05-20

Intelligent Decision Making Technology and Challenge of Wargame

YIN Qi-Yue^{1, 2
,},
ZHAO Mei-Jing^1
,,
NI Wan-Cheng^{1, 2
,},
ZHANG Jun-Ge^{1, 2
,},
HUANG Kai-Qi^{1, 2
,}

1.
Institute of Automation, Chinese Academy of Sciences, Beijing 100190
2.
University of Chinese Academy of Sciences, Beijing 100049

Funds: Supported by National Natural Science Youth Foundation of China (61906197)

More Information

Author Bio:
YIN Qi-Yue　Associate professor at the Institute of Automation, Chinese Academy of Sciences. His research interest covers reinforcement learning, data mining, and artificial intelligence on games

ZHAO Mei-Jing　Associate professor at the Institute of Automation, Chinese Academy of Sciences. Her research interest covers knowledge representation and modeling, and complex system decision-making

NI Wan-Cheng　Professor at the Institute of Automation, Chinese Academy of Sciences. Her research interest covers data mining and knowledge discovery, complex system modeling, and swarm intelligence platform and evaluation

ZHANG Jun-Ge　Professor at the Institute of Automation, Chinese Academy of Sciences. His research interest covers continuous learning, small sample learning, game decision making, and reinforcement learning

HUANG Kai-Qi　Professor at the Institute of Automation, Chinese Academy of Sciences. His research interest covers computer vision, pattern recognition, and cognitive decision-making. Corresponding author of this paper

摘要

摘要: 近年来, 以人机对抗为途径的智能决策技术取得了飞速发展, 人工智能(Artificial intelligence, AI)技术AlphaGo、AlphaStar等分别在围棋、星际争霸等游戏环境中战胜了顶尖人类选手. 兵棋推演作为一种人机对抗策略验证环境, 由于其非对称环境决策、更接近真实环境的随机性与高风险决策等特点, 受到智能决策技术研究者的广泛关注. 通过梳理兵棋推演与目前主流人机对抗环境(如围棋、德州扑克、星际争霸等)的区别, 阐述了兵棋推演智能决策技术的发展现状, 分析了当前主流技术的局限与瓶颈, 对兵棋推演中的智能决策技术研究进行了思考, 期望能对兵棋推演相关问题中的智能决策技术研究带来启发.
- 兵棋推演 /
- 人机对抗 /
- 智能决策技术 /
- 博弈学习
Abstract: In recent years, decision-making intelligence based on human-machine confrontation has achieved rapid development. For example, artificial intelligence (AI) technology such as AlphaGo and AlphaStar have defeated top human players in games Go and StarCraft, respectively. Nowadays, wargame, as a new verification environment for human-machine confrontation, attracts more and more researchers due to new challenges being raised, i.e., asymmetric environmental decision-making and randomness with high-risk decision-making. In this paper, we will sort out the differences between wargame and the current mainstream human-machine confrontation environments such as Go, Poker and StarCraft. Then, we explain the development status of wargame intelligent technology, and analyze the limitations of current mainstream technologies. Finally, we present our thoughts about future development of technologies for wargame, hoping to inspire researchers for through study on wargame.
- Wargame /
- human-machine confrontation /
- intelligent decision making technology /
- game learning
注释:

1) ¹ http://turingai.ia.ac.cn

2) ² http://turingai.ia.ac.cn/ranks/wargame_list

3) ³ http://turingai.ia.ac.cn/notices/detail/116

4) ⁴ http://turingai.ia.ac.cn/bbs/detail/14/1/29

5) ⁵ http://www.cas.cn/syky/202107/t20210712_4798152.shtml

6) ⁶ http://gym.openai.com

HTML全文

图 1 包以德循环

Fig. 1 OODA loop

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图 2 自博弈加强化学习训练

Fig. 2 Self-gaming and reinforcement learning

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图 3 IMAPLA用于兵棋推演智能体训练

Fig. 3 IMAPLA for training wargame agents

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图 4 知识与数据驱动“加性融合”框架

Fig. 4 Additive fusion framework of knowledge and data driven

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图 5 人机对抗框架^[45]

Fig. 5 Human-machine confrontation framework^[45]

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图 6 知识与数据驱动“主从融合”框架

Fig. 6 Principal and subordinate fusion framework of knowledge and data driven

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图 7 智能体单项能力评估

Fig. 7 Evaluation of specific capability of agents

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图 8 “图灵网”平台

Fig. 8 Turing website platform

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图 9 兵棋推演知识库构建示例

Fig. 9 Example of knowledge base construction for wargame

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图 10 兵棋推演中的异步协同与同步协同对比

Fig. 10 Comparison between asynchronous cooperation and synchronous cooperation in wargame

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图 11 兵棋推演大模型训练挑战

Fig. 11 Challenges of training big model for wargame

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图 12 排兵布阵问题示例

Fig. 12 Example for problem of arranging arms

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图 13 异步协同问题示例

Fig. 13 Example for problem of asynchronous multi-agent cooperation

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表 1 对决策带来挑战的代表性因素

Table 1 Representative factors of challenge decision-making

游戏	雅达利	围棋	德州扑克	星际争霸	兵棋推演
不完美信息博弈	√	×	√	√	√
长时决策	√	√	×	√	√
策略非传递性	×	√	√	√	√
智能体协作	×	×	×	√	√
非对称环境	×	×	×	×	√
随机性与高风险	×	×	×	×	√

下载: 导出CSV

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