Level Abstraction

Level abstraction research focuses on representing complex systems and problems using simpler, higher-level models to improve efficiency and understanding. Current efforts concentrate on developing algorithms and model architectures that learn and utilize these abstractions, for example, through distribution-based representations of concepts or higher-order refactoring of logic programs, to solve problems in areas like abstract reasoning and reinforcement learning. This work has significant implications for improving the efficiency and scalability of AI systems, particularly in complex domains where traditional methods struggle, and for enabling safer and more robust control in robotics and other real-world applications.