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Activity, Plan, and Goal Recognition: A Review.

Franz A Van-Horenbeke1, Angelika Peer1

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Summary
This summary is machine-generated.

Understanding human actions, plans, and goals is crucial for natural human-robot interaction. This review explores current artificial intelligence approaches to action, plan, and goal recognition, highlighting research gaps for unified solutions.

Keywords:
action recognitionactivity recognitionbehavior recognitiongoal recognitionhuman-computer interactionhuman-machine interactionhuman-robot interactionplan recognition

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Human-like interaction with robots requires recognizing human actions, plans, and goals in real-world environments.
  • Current artificial intelligence (AI) struggles with the complexity and variety of human behaviors in unconstrained settings.
  • Existing AI techniques often focus on isolated aspects like action recognition, rather than a holistic approach to understanding intentions.

Purpose of the Study:

  • To provide a comprehensive overview of activity, plan, and goal recognition as a unified problem.
  • To describe the challenges from both human and computational perspectives.
  • To classify and compare existing AI approaches for intention recognition.

Main Methods:

  • Literature review of existing research on activity, plan, and goal recognition.
  • Categorization of approaches into logic-based, classical machine learning, deep learning, and brain-inspired methods.
  • Comparative analysis of the strengths and weaknesses of each approach.

Main Results:

  • Identified a gap in unified approaches that address the entirety of action, plan, and goal recognition.
  • Classified diverse AI methodologies applied to intention recognition.
  • Highlighted the limitations of current methods in achieving human-level performance.

Conclusions:

  • A unified approach to activity, plan, and goal recognition is needed for advanced human-robot interaction.
  • Further research can bridge identified gaps by developing integrated computational models.
  • This review serves as a foundation for future research directions in intention recognition for AI.