  In the context of real-time planning, this paper investigates the
contributions of two enhancements for selecting actions. First, the
agenda-driven planning enhancement ranks relevant atomic goals and solves them
incrementally in a best-first manner. Second, the committed jump enhancement
commits a sequence of actions to be executed at the following time steps. To
assess these two enhancements, we developed a real-time planning algorithm in
which action selection can be driven by a goal-agenda, and committed jumps can
be done. Experimental results, performed on classical planning problems, show
that agenda-planning and committed jumps are clear advantages in the real-time
context. Used simultaneously, they enable the planner to be several orders of
magnitude faster and solution plans to be shorter.

Artificial Intelligence