知识牵引与数据驱动的兵棋AI设计及关键技术

在分析知识推理型与数据学习型兵棋人工智能(artifical intelligence, AI)优缺点的基础上, 提出了基于知识牵引与数据驱动的AI设计框架。针对框架中涉及的基于数据补全的战场态势感知，基于遗传模糊系统的关键点推理，基于层次任务网的任务规划、计划修复与重规划，基于深度强化学习的算子动作策略优化等关键技术进行深入探讨。结果表明，所提框架具有较强的适应性, 不仅能够满足分队、群队、人机混合等兵棋推演的应用需求, 而且适用于解决一般回合制或即时策略性的博弈对抗问题。

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

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.