Towards an Integrated Learning Framework for Behavior Modeling of Adaptive CGFs

Abstract

Computer generated forces (CGFs) are autonomous or semi-autonomous actors within military, simulation based, training and analyzing applications. Rapid, realistic and adaptive behavior modeling for CGFs is imperative and challenging. Traditional modeling approaches like rule-based script usually need time-consuming, repetitive endeavor and result in rigid, predictable behavior performance. Recent developments introducing Machine Learning (ML) techniques, such as dynamic script or neural network models, always present as black box systems, which are difficult to understand and revise for subject matter experts (SMEs). To overcome these limitations, we propose an integrated learning framework to facilitate adaptive CGF behavior modeling. The framework represents domain knowledge explicitly as Behavior Trees (BTs), and integrates learning BTs automatically from demonstration and Reinforcement Learning (RL) node into BTs. Besides, a CBR-style planner is adopted to retrieve executable behavior for diverse situations encountered at runtime. Through aforementioned components, the framework can make full use of the advantages of various learning approaches and knowledge sources to generate realistic and adaptive behaviors for CGFs easily.