  Behavior trees (BTs) emerged from video game development as a graphical
language for modeling intelligent agent behavior. However as initially
implemented, behavior trees are static plans. This paper adds to recent
literature exploring the ability of BTs to adapt to their success or failure in
achieving tasks. The "Selector" node of a BT tries alternative strategies (its
children) and returns success only if all of its children return failure. This
paper studies several means by which Selector nodes can learn from experience,
in particular, learn conditional probabilities of success based on sensor
information, and modify the execution order based on the learned iformation.
Furthermore, a "Greedy Selector" is studied which only tries the child having
the highest success probability. Simulation results indicate significantly
increased task performance, especially when frequentist probability estimate is
conditioned on sensor information. The Greedy selector was ineffective unless
it was preceded by a period of training in which all children were exercised.
