知识牵引与数据驱动的兵棋AI设计及关键技术

doi:10.12305/j.issn.1001-506X.2021.10.26

Abstract

Abstract:

Based on the analysis of the advantages and disadvantages of knowledge-based reasoning and data-learning wargame artifical intelligence (AI), an AI design framework based on knowledge traction and data-driven is proposed. The key technologies involved in the framework such as battlefield situation awareness based on data completion, key point reasoning based on genetic fuzzy system, mission planning based on hierarchical task network, plan repair and replanning, and operator action strategy optimization based on deep reinforcement learning are discussed in depth. The result shows that the proposed framework is highly adaptable. It can not only meet the application requirements of team, group, man-machine mixed wargaming, but also be suitable for solving general turn-based or real-time strategic game confrontation problems.

Key words: knowledge, data, wargame, situational awareness, mission planning, reinforcement learning

CLC Number:

TP181

Kai CHENG, Gang CHEN, Xiaohan YU, Man LIU, Tianhao SHAO. Knowledge traction and data-driven wargame AI design and key technologies[J]. Systems Engineering and Electronics, 2021, 43(10): 2911-2917.

Figures/Tables 8

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References 32

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Knowledge traction and data-driven wargame AI design and key technologies

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