Discrete Abstraction

Discrete abstraction in artificial intelligence focuses on enabling systems to learn simplified, discrete representations of inherently continuous problems, facilitating efficient decision-making and control. Current research emphasizes hybrid models, combining continuous controllers with discrete planners, often utilizing architectures like recurrent switching linear dynamical systems (rSLDS) to learn meaningful discrete abstractions from data. These methods aim to improve learning speed, enhance exploration strategies, and enable hierarchical planning by identifying temporally-extended actions or subgoals. The resulting advancements have implications for various fields, including robotics and control systems, by enabling more robust and efficient solutions to complex tasks.