Learning STRIPS Action Models with Classical Planning

Understand

This paper presents a novel approach for learning STRIPS action models from examples that compiles this inductive learning task into a classical planning task.

• Interestingly, the compilation approach is flexible to different amounts of available input knowledge; the learning examples can range from a set of plans (with their corresponding initial and final states) to just a pair of initial and final states (no intermediate action or state is given).
• Moreover, the compilation accepts partially specified action models and it can be used to validate whether the observation of a plan execution follows a given STRIPS action model, even if this model is not fully specified.