Solving Complex Planning Tasks Through Extraction of Subproblems

The paper introduces an approach to derive a total ordering between increasing sets of subgoals by defining a relation over atomic goals. The ordering is represented in a so-called goal agenda that is used by the planner to incrementally plan for the increasing sets of subgoals. This can lead to an exponential complexity reduction because the solution to a complex planning problem is found by solving easier subproblems. Since only a polynomial overhead is caused by the goal agenda computation, a potential exists to dramatically speed up planning algorithms as we demonstrate in the empirical evaluation.