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8.2  Using Several Methods to Enhance the Goals

In order to measure the effectiveness of the three proposed methods to enhance the goals, we ran Grt using them in the logistics problems of the Aips-00 competition. We selected this domain, since in the other domains of the competition the goal state is either complete, or near complete, so there is no difference among the three methods. Figure 8 shows the solution length and time for the easiest of the logistics problems.

With regard to solution length, the first method, which considers all the candidate facts as goal facts, always came up with better plans. As we mentioned in Section 3.2, this method produces small differences among the estimated distances, so the search process tends to be breadth-first. However, in most of the cases, the third method found plans of equal quality. With regard to the solution time, the last two methods work faster, since they produce greater differences between the distances.

In Section 3.3 we also presented a method of enriching the domain representation. As already mentioned, we were motivated by the need to treat domains like the movie or the elevator. We do not present comparative performance results between the domain enrichment method and the pure Grt planner for these domains, since without this technique it is impossible for Grt to solve the problems. However, it would be interesting to test the efficiency of this method to other heuristic state space planners.

Figure 8: Results for logistics problems using different methods to complete the goals.
All = Consider all the candidate facts as goal facts.
Initial = Select the initial state facts.
Greedy = Favor the most promising facts.

 

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Ioannis Refanidis

14-8-2001