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8.6.1.        Logistics

For the logistics domain we used the test suite of the Aips-00 competition. The results are shown in Figure 14. In this domain Grt, as well as Ff and Stan, performed well, solving all the problems. Hsp and Altalt failed to solve the large problems within the time-limit. In general, best plans are found by Stan, which uses special domain-dependent heuristics for problems identified as transportation problems. Best solution times are achieved by Ff and Stan in the small problems and by Grt in the large ones.

 

Figure 14: Solution length and time (msecs) for the logistics problems of the Aips-00 competition.

The logistics problems in Figure 14 have incomplete goal states. Grt ran with the most promising facts goals-completion method and with the hill-climbing strategy. However, the incompleteness of the goal state is an advantage for the planners that construct the heuristic in a forward direction. Motivated by this remark, we forced all the planners to solve logistics problems with complete goal states, requiring all the trucks and planes to return to their initial location. The results are shown in Figure 15.

 

Figure 15: Solution length and time (msecs) for logistics problems with complete goal states.

In the new logistics problems, Grt, Stan and Hsp-2 exhibited stable performance, solving the problems in about the same time. For Grt, this means that the goal completion mechanism behaves well, at least in this domain. Ff failed to solve the large problems. Finally, Altalt solved some more problems and this is because the regression mechanism did not encounter invalid states. Note that, although the goal state was complete in this case, Grt treated these problems as usual, attempting to enhance the goals.

 

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Ioannis Refanidis

14-8-2001