Aberrant Behavior

Aberrant behavior detection encompasses identifying deviations from expected patterns across diverse systems, from individual human actions to complex multi-agent interactions. Current research focuses on developing robust algorithms and models, including deep learning architectures like those employing multi-level guidance and pattern-based anomaly detection, to accurately and efficiently flag such anomalies in real-time. These advancements have significant implications for various fields, improving safety in autonomous systems, enhancing online exam proctoring, and aiding in the diagnosis and monitoring of conditions like autism and opioid addiction. The development of effective anomaly detection methods is crucial for improving safety, security, and healthcare.