Exploiting First-Order Regression in Inductive Policy Selection
  We consider the problem of computing optimal generalised policies for
relational Markov decision processes. We describe an approach combining some of
the benefits of purely inductive techniques with those of symbolic dynamic
programming methods. The latter reason about the optimal value function using
first-order decision theoretic regression and formula rewriting, while the
former, when provided with a suitable hypotheses language, are capable of
generalising value functions or policies for small instances. Our idea is to
use reasoning and in particular classical first-order regression to
automatically generate a hypotheses language dedicated to the domain at hand,
which is then used as input by an inductive solver. This approach avoids the
more complex reasoning of symbolic dynamic programming while focusing the
inductive solver's attention on concepts that are specifically relevant to the
optimal value function for the domain considered.
