Learning First-Order Representations for Planning from Black-Box States:
  New Results
  Recently Bonet and Geffner have shown that first-order representations for
planning domains can be learned from the structure of the state space without
any prior knowledge about the action schemas or domain predicates. For this,
the learning problem is formulated as the search for a simplest first-order
domain description D that along with information about instances I_i (number of
objects and initial state) determine state space graphs G(P_i) that match the
observed state graphs G_i where P_i = (D, I_i). The search is cast and solved
approximately by means of a SAT solver that is called over a large family of
propositional theories that differ just in the parameters encoding the possible
number of action schemas and domain predicates, their arities, and the number
of objects. In this work, we push the limits of these learners by moving to an
answer set programming (ASP) encoding using the CLINGO system. The new
encodings are more transparent and concise, extending the range of possible
models while facilitating their exploration. We show that the domains
introduced by Bonet and Geffner can be solved more efficiently in the new
approach, often optimally, and furthermore, that the approach can be easily
extended to handle partial information about the state graphs as well as noise
that prevents some states from being distinguished.
