heuristic assumption that if the analysis found no reasonable goal orderings, then the goals are achievable in any order. With this option, the problems are solved almost instantly.

The reader may wonder at this point why we use linearization of agenda entries only as an extra option and do not investigate it further. There are two reasons for that. First, linearization does have negative side effects in most domains that we investigated.  For example, it yields much longer plans in the logistics domain and all its variants. When linearizing the single entry that the agenda for a logistics problem contains, all packages get transported to their goal position one by one.  Of course, this takes much more planning steps than simultaneously transporting packages with coinciding destinations.

Secondly, the effects of linearization are somewhat unpredictible, even in domains where it usually tends to yield good results. This is because GAM does not recognise all interactions between goals. Consider a blocks world problem with four blocks A, B, C and D.

Say B is positioned on C initially, the other blocks being each on the table, and the goal is to have on ( A, B ) and on ( C, D ). The agenda for this problem will comprise a single entry containing both goals. In fact, there is no reasonable goal ordering here. Nevertheless, staing A onto B immedeatly is a bad idea, as the planner needs to move C to aieve on(C, D).  Being not aware of this, GAM might linearize the single agenda entry to have on(A, B) up front, which makes the problem harder than it actually is. Thus, the runtime advantages that linearization sometimes yields on the blocks world can be more or less seen as cases of "good luck".

Figure 10 shows IPP on the stack n problems. IPP without any domain analysis can handle up to 12 blocks in less than 5 minutes, but for 13 blocks more than 15 minutes are nneeded. Using GAM, 40 blocks can be stacked in less than 5 minutes. Using GAM and RIFO, the 5 minutes limit is extended to 80 blocks, while stack100 is solved in 11.5 min where 11.3 min are spent for both analysis methods and only 0.2 min are needed for building the planning graphs and extracting a plan.

![0_image_0.png](0_image_0.png)

Figure 11 shows the sharing of the overall problem-solving time between GAM, RIFO
and the IPP search algorithm on blocks world problems. Similar results are obtained in the tyreworld.  GAM takes between 3 and 16 %, RIFO takes between 75 and 96 %, and the sseener the state of the control determined by RIFO, while the search effort becomes a marginal factor in the determination of performance.  This indicates that a further speed-up is possible when improving the