This article presents the GRT planner, a forward heuristic state-space planner, and comments on the results obtained from the Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS'00) planning competition. The grt planner works in two phases. In the preprocessing phase, it estimates the distances between the facts and the goals of the problem. During the search phase, the estimates are used to guide a forward-directed search.

The main idea that arise during the forward search phase and of the planner is to compute offline, in the preprocessing the goals. This approach succeeds in the notion of related facts in the goal-regression avoiding computing estimates for invalid facts process. These are facts that have been achieved in the preprocessing phase. However, it introduces either by the same or subsequent actions, without some problems in situations where the the last actions deleting the facts achieved goal state is not completely described because first. The cost of achieving simultaneously a set an action to regress the goals might not exist. of unrelated facts is considered equal to the To cope with this situation, at the beginning sum of their individual costs, whereas the cost of the preprocessing phase, We know from our experience that if move actions were Table 1.