State Abstraction

State abstraction simplifies complex systems by reducing the number of states considered, improving efficiency and generalization in tasks like reinforcement learning and control systems verification. Current research focuses on developing data-driven methods for constructing these abstractions, particularly for high-dimensional or continuous state spaces, often employing techniques like neural networks, Markov decision processes (MDPs), and hierarchical clustering. This work is significant because it enables the application of AI and control algorithms to real-world problems previously intractable due to computational complexity, with applications ranging from robotics and autonomous driving to healthcare and game playing.