First, it formally defines and discusses two different goal orderings, which are called the reasonable and the forced ordering. The complexity of these orderings is investigated and their practical relevance is discussed. Secondly, two different methods to compute reasonable goal orderings are developed. One of them is based on planning graphs, while the other investigates the set of actions directly. Finally, it is shown how the ordering relations, which have been derived for a given set of goals G, can be used to compute a so-called goal agenda that divides G into an ordered set of subgoals.
Any planner can then, in principle, use the goal agenda to plan for increasing sets of subgoals.
This can lead to an exponential complexity reduction, as 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, where we use this method in the IPP planner. AI planning technology offers a very elegant and flexible solution because the possible actions of a control system can be made explicit and their preconditions and effects can be specified using expressive representation formalisms. Based on the specification, a planner can flexibly synthesize the required control and changes in the specification do not require any reimplementation of the control software. In this paper, we describe the application and investigate how currently available domain-independent planning formalisms can cope with it.