

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

which each sub-problem involves the independent transportation of a single package between two cities.

In very simple domains, the overhead of carrying out this analysis can outweigh the advantages offered. For example, in the Movie domain used in the competition stan gained no benefits from using TIM but paid the overhead to the detriment of its performance on instances from that domain. However, in general we observe that the benefits of this analysis increase with the increasing complexity of domains.

## 5. Related Work

Although the importance of state invariants for efficient planning has been observed there has been relatively little work on automatic inference of invariants.  The published work that most closely resembles the research described in this paper is the state constraint inference ssssseeee of sv-constraints that correspond to a subset of our identity invariants. The reason that DISCOPLAN is restricted to a subset is that it generates sv-constraints only for pairs of literals
(one on the addlist of a schema and the other on the delete list) in which the arguments vary in only one place. Tim can inferidentity invariants in which vectors of arguments vary, as shown in Section 2.7. Discoplan cannot currently infer all singly varying constraints
(although the techniques described by Gerevini and Schubert (1996a) are not yet fully implemented in discoplan). For example, discoplan cannot inferthat all blocks can only