only one move operator and the blocks world. In both domains, ALPINE cannot detect the orderings because it investigates the operator schemata, not the set of ground actions, and therefore cannot distinguish the orderings between different instantiations of the same lteral. Although ALPINE could be modified to handle ground actions, this will significantly increase the amount of computation it requires. GAM on the other hand, handles large sets of ground actions in an efficient way, in particular if direct analysis is used. 10 An analysis, which is quite similar to ALPINE, but which is performed in the framework of HTN planning, is described by Tsuneto et al. (1998).  The approach analyzes the external conditions of methods, which cannot be achieved when decomposing the method further.

This means, such conditions have to be established by the decomposition of those methods, which precede the method using this external condition. Two strategies to determine the decomposition order of methods are defined and empirically compared. Here lies the main difference to the other approaches described so far: Instead of trying to automatically construct the decomposition orderings, they are predefined and fixed for all domains and problems.

Harmful interactions among operators are studied by Smith and Peot (1993) and Etzioni
(1993). A threat of an operator o to a precondition p occurs if there is an instantiation of o such that its effects are inconsistent with p (Smith & Peot, 1993). The knowledge about threats is used to control a plan-space planner. In contrast to a state-space planner such as IPP, computing an explicit ordering of goals does not prevent the presence of threats in a partial plan because the order in which the goals are processed does not determine the order in which actions occur in the plan. The notion of forced and reasonable goal orderings is not comparable to that of a threat because a threat still has the potential of being resolved by adding binding or ordering constraints to the plans. In contrast to this, a forced or reasonable goal ordering persists under all bindings and enforces a specific ordering of the subgoals.

Given a planning problem, STATIC (Etzioni, 1993) computes a backchaining tree from the goals in the form of an AND/OR graph, which it subsequently analyzes for the occurrence of goal interactions that will necessarily occur.  This analysis is much more complicated than ours, because STATIC has to deal with uninstantiated operators and axioms, which describe properties of legal states. The result of the analysis are goal ordering rules, which order goals if certain conditions are satisfied in a state.  This is the main difference to GAM,
which generates explicit goal orderings independently of a specific state. It does not need to extract conditions that a specific state has to satisfy because it considers the generic state 8(A,-B) in the analysis, which represents all states satisfying A, but not B. As GAM, STATIC
is incomplete in the sense that it cannot detect all existing goal interactions. The problem for GAM is that deciding reasonable orderings is PSPACE-hard, as we have proven in this paper. The problem for STATIC is that it has to compute the necessary effects of an operator in a given state. As Etzioni (1993) conjectures and Nebel and Bäckström (1994) prove, this