General policies represent reactive strategies for solving large families of
planning problems like the infinite collection of solvable instances from a
given domain. Methods for learning such policies from a collection of small
training instances have been developed successfully for classical domains. In
this work, we extend the formulations and the resulting combinatorial methods
for learning general policies over fully observable, non-deterministic (FOND)
domains. We also evaluate the resulting approach experimentally over a number
of benchmark domains in FOND planning, present the general policies that result
in some of these domains, and prove their correctness. The method for learning
general policies for FOND planning can actually be seen as an alternative FOND
planning method that searches for solutions, not in the given state space but
in an abstract space defined by features that must be learned as well.

扩展学习通用策略的公式和组合方法以解决完全可观察、非确定性（FOND）领域的规划问题，通过实验证实所得方法在多个 FOND 规划基准领域上，并验证了其正确性。学习 FOND 规划的通用策略方法可以被视为在抽象空间中寻找解决方案的一种替代 FOND 规划方法，该抽象空间由需要学习的特征定义。