Graph-based autonomy behavior classification

John E. Kleider; Glen Madsen

doi:10.1117/12.2662884

12 June 2023 Graph-based autonomy behavior classification

John E. Kleider, Glen Madsen

Author Affiliations +

Proceedings Volume 12544, Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2023; 1254406 (2023) https://doi.org/10.1117/12.2662884
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

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.

Conference Presentation

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John E. Kleider and Glen Madsen "Graph-based autonomy behavior classification", Proc. SPIE 12544, Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2023, 1254406 (12 June 2023); https://doi.org/10.1117/12.2662884

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