Human Behavior
Human behavior research aims to understand and model the complexities of human actions and decision-making, leveraging diverse data sources and advanced computational techniques. Current research focuses on using large language models (LLMs), agent-based models (ABMs), and multimodal machine learning to simulate and predict behavior across various contexts, from industrial settings to social interactions and autonomous driving. These efforts are significant for improving human-computer interaction, optimizing organizational efficiency, and enhancing the safety and reliability of autonomous systems, among other applications. The field faces challenges in ensuring the realism, generalizability, and ethical implications of these models.
Papers
Challenges of Data-Driven Simulation of Diverse and Consistent Human Driving Behaviors
Kalle Kujanpää, Daulet Baimukashev, Shibei Zhu, Shoaib Azam, Farzeen Munir, Gokhan Alcan, Ville Kyrki
CogGPT: Unleashing the Power of Cognitive Dynamics on Large Language Models
Yaojia Lv, Haojie Pan, Zekun Wang, Jiafeng Liang, Yuanxing Liu, Ruiji Fu, Ming Liu, Zhongyuan Wang, Bing Qin