Artificial Behavior Intelligence: Technology, Challenges, and Future Directions

TL;DR

This paper introduces Artificial Behavior Intelligence (ABI), a comprehensive framework for analyzing human behavior using advanced AI models, highlighting recent progress, challenges, and future research directions for real-world applications.

Contribution

It defines the ABI framework, reviews recent AI advances in behavior recognition, and discusses technical challenges and potential solutions for deploying ABI in practical settings.

Findings

Large-scale pretrained models improve behavior recognition accuracy.

Lightweight models enable real-time behavior inference.

Identified key challenges like data scarcity and model efficiency.

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.