  Definition of an accurate system model for Automated Planner (AP) is often
impractical, especially for real-world problems. Conversely, off-the-shelf
planners fail to scale up and are domain dependent. These drawbacks are
inherited from conventional transition systems such as Finite State Machines
(FSMs) that describes the action-plan execution generated by the AP. On the
other hand, Behavior Trees (BTs) represent a valid alternative to FSMs
presenting many advantages in terms of modularity, reactiveness, scalability
and domain-independence. In this paper, we propose a model-free AP framework
using Genetic Programming (GP) to derive an optimal BT for an autonomous agent
to achieve a given goal in unknown (but fully observable) environments. We
illustrate the proposed framework using experiments conducted with an open
source benchmark Mario AI for automated generation of BTs that can play the
game character Mario to complete a certain level at various levels of
difficulty to include enemies and obstacles.
