Artificial Behavior Intelligence: Technology, Challenges, and Future Directions
Journal:
arXiv
Published Date:
May 6, 2025
Abstract
Understanding and predicting human behavior has emerged as a core capability
in various AI application domains such as autonomous driving, smart healthcare,
surveillance systems, and social robotics. This paper defines the technical
framework of Artificial Behavior Intelligence (ABI), which comprehensively
analyzes and interprets human posture, facial expressions, emotions, behavioral
sequences, and contextual cues. It details the essential components of ABI,
including pose estimation, face and emotion recognition, sequential behavior
analysis, and context-aware modeling. Furthermore, we highlight the
transformative potential of recent advances in large-scale pretrained models,
such as large language models (LLMs), vision foundation models, and multimodal
integration models, in significantly improving the accuracy and
interpretability of behavior recognition. Our research team has a strong
interest in the ABI domain and is actively conducting research, particularly
focusing on the development of intelligent lightweight models capable of
efficiently inferring complex human behaviors. This paper identifies several
technical challenges that must be addressed to deploy ABI in real-world
applications including learning behavioral intelligence from limited data,
quantifying uncertainty in complex behavior prediction, and optimizing model
structures for low-power, real-time inference. To tackle these challenges, our
team is exploring various optimization strategies including lightweight
transformers, graph-based recognition architectures, energy-aware loss
functions, and multimodal knowledge distillation, while validating their
applicability in real-time environments.
