Graph-based autonomy behavior classification

Graph neural networks (GNNs) are becoming increasingly popular for multi-agent autonomy applications. For mission planning, when numerous autonomous agents are involved, even simple behaviors can overwhelm the human commander. This is because the number of possible interactions between agents and possible outcomes for coordinating effective teaming grows exponentially. Artificial intelligence can be used to learn effective agent behaviors to create what we define as a Virtual Commander (VC). The VC can be used to learn friendly and/or adversary behaviors in a complex mission planning scenario. A VC may be comprised of multiple machine learning components, such as behavior classification and behavior prediction. Based on classifying a particular behavior mode at any instant in time, future behaviors can be learned and therefore predicted to aid a commander in effective mission planning. In this work we compare 2 methods of behavior mode classification for a virtual commander using graphs. The first method uses an image-based digit (IBD) classifier for mode classification, while the second method uses adjacency matrix-based (AMB) classifier to perform mode classification. Though both classifiers predict the VC mode classification correctly <90% of the time, we show that the AMB performs better than IBD, while also reducing the data generation complexity for training and testing by multiple orders of magnitude.