Generalized planning is concerned with the computation of plans that solve
not one but multiple instances of a planning domain. Recently, it has been
shown that generalized plans can be expressed as mappings of feature values
into actions, and that they can often be computed with fully observable
non-deterministic (FOND) planners. The actions in such plans, however, are not
the actions in the instances themselves, which are not necessarily common to
other instances, but abstract actions that are defined on a set of common
features. The formulation assumes that the features and the abstract actions
are given. In this work, we address this limitation by showing how to learn
them automatically. The resulting account of generalized planning combines
learning and planning in a novel way: a learner, based on a Max SAT
formulation, yields the features and abstract actions from sampled state
transitions, and a FOND planner uses this information, suitably transformed, to
produce the general plans. Correctness guarantees are given and experimental
results on several domains are reported.

本文介绍了一种结合自动学习和规划的方法来解决泛化计划问题，主要通过 Max SAT 表达式从已知实例的状态变迁中自动学习特征和抽象动作，并使用 fully observable 非确定性规划器生成泛化计划。实验结果展示了该方法的有效性。