  In this paper, we show how Behavior Trees that have performance guarantees,
in terms of safety and goal convergence, can be extended with components that
were designed using machine learning, without destroying those performance
guarantees.
  Machine learning approaches such as reinforcement learning or learning from
demonstration can be very appealing to AI designers that want efficient and
realistic behaviors in their agents. However, those algorithms seldom provide
guarantees for solving the given task in all different situations while keeping
the agent safe. Instead, such guarantees are often easier to find for manually
designed model-based approaches. In this paper we exploit the modularity of
behavior trees to extend a given design with an efficient, but possibly
unreliable, machine learning component in a way that preserves the guarantees.
The approach is illustrated with an inverted pendulum example.
