群队级兵棋实体智能行为决策方法研究

计算机兵棋能够对敌对双方或多方的军事行动进行模拟, 为军事智能决策的研究提供贴近真实战争的决策背景和试验环境。随着被控制棋子类型和数量增加, 这些作战实体的行为决策控制将面临巨大的挑战。本文提出了知识与数据互补的兵棋行为决策算法, 针对较多作战实体(50个左右)的群队级兵棋, 提出了按实体类型分组决策、作战目标指引、位置评估指标并行计算等方法, 设计了群队级兵棋人工智能(artificial intelligence, AI)技术框架, 并实现了一个群队级兵棋AI。该兵棋AI将知识、数据和学习融为一体, 具有设计简单、迁移能力强、决策灵活等优点。

Research on behavior decision-making of multi entities in group-level wargame

Computer wargame can simulate the military actions of two or more opposed parties, which provides a decision-making background and experimental environment close to the real war for the research of military intelligent decision-making. With the increase of the type and number of controlled entities, the intelligent behavior decision-making will face great challenges. This paper proposes the behavior decision-making algorithm by complementary knowledge and data for group-level wargame which has multi entities (about 50), including decision-making grouped as entity type, operational target allocation guidance, parallel calculation of position evaluation. Then, we design the framework of group-level wargame artificial intelligence (AI) technology, and realizes it. The wargame AI integrates knowledge, data and learning, has the advantages of less design workload, strong migration ability and flexible decision-making.