Prediction of intent in robotics and multi-agent systems

Area of Science:

• Artificial Intelligence
• Robotics
• Human-Computer Interaction

Background:

• Observable agent behavior is a limited proxy for understanding underlying intent.
• Intent recognition is vital for effective collaboration and competition in AI systems.
• Applications span assistive robotics, gaming, and intelligent tutoring.

Purpose of the Study:

• To review and analyze approaches for action recognition and intent prediction.
• To examine these methods from a multi-disciplinary perspective.
• To focus on generative approaches in both single and multi-agent scenarios.

Main Methods:

• Literature review of multi-disciplinary research.
• Analysis of action recognition techniques.
• Examination of intent prediction methodologies, with a focus on generative models.

Main Results:

• Identified key challenges in intent recognition.
• Highlighted the strengths of generative approaches.
• Provided a comprehensive overview of current research.

Conclusions:

• Generative approaches show significant promise for accurate intent prediction.
• Further research is needed to address the complexities of multi-agent intent recognition.
• Improved intent understanding will enhance AI capabilities in human-centric applications.