Action Abstraction

Action abstraction in artificial intelligence focuses on simplifying complex decision-making problems by representing actions at different levels of detail, improving efficiency and generalization in planning and control. Current research emphasizes learning these abstractions automatically from data, often using techniques like reinforcement learning, Monte Carlo Tree Search, and language models to discover hierarchical action structures and latent state representations. This work is significant because effective action abstraction is crucial for enabling agents to solve long-horizon tasks, handle large action spaces, and generalize to novel situations in robotics, game playing, and other domains.