Learning Generalized Policies for Fully Observable Non-Deterministic
  Planning Domains
Till Hofmann Hector Geffner
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.