Novel Behavior

Research on novel behavior focuses on identifying and understanding unexpected actions or patterns in diverse systems, from computer programs and language models to human activity and evolving algorithms. Current approaches leverage machine learning techniques, including language models (LSTMs, Transformers, Longformers), Hawkes point processes for time-series analysis, and evolutionary algorithms incorporating novelty search, to detect and explain these behaviors. This work is significant for improving system reliability, enhancing human-computer interaction, and providing new insights into the dynamics of complex systems, ultimately leading to more robust and adaptable technologies.