  When we exercise sequences of actions, their execution becomes more fluent
and precise. Here, we consider the possibility that exercised action sequences
can also be used to make planning faster and more accurate by focusing
expansion of the search tree on paths that have been frequently used in the
past, and by reducing deep planning problems to shallow ones via multi-step
jumps in the tree. To capture such sequences, we use a flexible Bayesian action
chunking mechanism which finds and exploits statistically reliable structure at
different scales. This gives rise to shorter or longer routines that can be
embedded into a Monte-Carlo tree search planner. We show the benefits of this
scheme using a physical construction task patterned after tangrams.
