Knowledge-Based and Data-Driven Integrating Design Methodology for Air Combat Strategy in Multi-Opponent Adversarial Game
The rapid development of artificial intelligence technology has endowed autonomous air combat strategies with the potential to surpass human experts.Existing intelligent air combat strategies can be classified into two categories based on their driving methods:knowledge-based strategies,which heavily rely on application scenarios and expert knowl-edge;and data-driven strategies,represented by reinforcement learning,which have poor interpretability and weak general-ization.In this study,focusing on the scenario of multi-agent cooperative air combat from the air intelligence game(AIG)—a knowledge-based and data-driven integrating strategy design method is proposed.The knowledge-based part utilizes ex-pert knowledge to design a parameterized and stylized knowledge-based artificial intelligence(AI)system,which generates high-quality offline data and initializes the strategy.The data-driven part employs graph attention networks to selectively represent information about teammates and opponents,aiming to improve training efficiency and convergence performance.Furthermore,a dynamic opponent matching mechanism is introduced for multi-agent reinforcement learning training to en-hance strategy generalization.The proposed strategy achieved a statistical winning rate of over 70%when competing against 12 teams from the top 16 teams in AIG.It is worth mentioning that these teams all adopt the latest knowledge-based or data-driven methods,with diverse styles,and at the same time,they have strong combat capabilities.
reinforcement learningknowledge and data integratingair combatmulti-opponentgeneralization
万方数据
维普
