基于多层次 LSTM 网络的多智能体攻防效能动态预测模型

Considering the difficulty of accurately predicting the operational effectiveness of multi-agent attack and defense (MAAD) systems due to multi-level coupling and irregular emergence, a dynamic prediction model based on multi-level LSTM network is constructed. The overall framework and operational process of the MAAD are clarified. Then, the multi-agent NetLogo platform is used to simulate the attack and defense confrontation process of red and blue agents in order to obtain multi-level evolutionary data of population structure and operational effectiveness when different decisions are made. On this basis, long short-term memory (LSTM) networks, which are effective in processing temporal features, are adopted to characterize the function mapping among the three layers of “individual decision, population structure, and operational effectiveness”, and to further predict the future attack and defense operational effectiveness and process based on the mapping relationship. The above modeling method has been proved to be feasible and effective in multiple simulations. The experimental results show that the prediction error of the model is only within 7%, which can serve as an effective guide for operational command and system development in MAAD systems.