CaMeL: Learning method preconditions for HTN planning

A great challenge in using any planning system to solve real-world problems is the difficulty of acquiring the do- main knowledge that the system will need. We present a way to address part of this problem, in the context of Hierarchical Task Network (HTN) planning, by having the planning system incrementally learn conditions for HTN methods under expert supervision. We present a general formal framework for learning HTN methods, and a supervised learning algorithm, named CaMeL, based on this formalism. We present theoretical results about CaMeL’s soundness, completeness, and conver- gence properties. We also report experimental results about its speed of convergence under different condi- tions. The experimental results suggest that CaMeL has the potential to be useful in real-world applications.