Self-organizing neural networks for learning air combat maneuvers

Abstract

This paper reports on an agent-oriented approach for the modeling of adaptive doctrine-equipped computer generated force (CGF) using a commercial-grade simulation platform known as CAE STRIVE®CGF. A self-organizing neural network is used for the adaptive CGF to learn and generalize knowledge in an online manner during the simulation. The challenge of defining the state space and action space and the lack of domain knowledge to initialize the adaptive CGF are addressed using the doctrine used to drive the non-adaptive CGF. The doctrine contains a set of specialized knowledge for conducting 1-v-1 dogfights. The hierarchical structure and symbol representation of the propositional rules are incompatible to the self-organizing neural network. Therefore, it has to be flattened and then translated to vector pattern before it can inserted into the self-organizing neural network. The state space and action space are automatically extracted using the flattened doctrine as well. Experiments are conducted using several initial conditions in round robin fashions. The experimental results show that the selforganizing neural network is able to make good use of the domain knowledge with complex knowledge structure to discover the knowledge to out-maneuver the doctrine-driven CGF consistently in an efficient manner.