  Symbolic task planning for robots is computationally challenging due to the
combinatorial complexity of the possible action space. This fact is amplified
if there are several sub-goals to be achieved due to the increased length of
the action sequences. In this work, we propose a multi-goal symbolic task
planner for deterministic decision processes based on Monte Carlo Tree Search.
We augment the algorithm by prioritized node expansion which prioritizes nodes
that already have fulfilled some sub-goals. Due to its linear complexity in the
number of sub-goals, our algorithm is able to identify symbolic action
sequences of 145 elements to reach the desired goal state with up to 48
sub-goals while the search tree is limited to under 6500 nodes. We use action
reduction based on a kinematic reachability criterion to further ease
computational complexity. We combine our algorithm with object localization and
motion planning and apply it to a real-robot demonstration with two
manipulators in an industrial bearing inspection setting.
