Analysing and Combining Static Pruning Techniques for Classical Planning Tasks

In classical AI planning, the state explosion problem is a reoccurring subject: although the problem descriptions are compact, often a huge number of states needs to be considered. One way to tackle this problem is to use static pruning methods which reduce the number of variables and operators in the … See more

In classical AI planning, the state explosion problem is a reoccurring subject: although the problem descriptions are compact, often a huge number of states needs to be considered. One way to tackle this problem is to use static pruning methods which reduce the number of variables and operators in the problem description before planning.In this work, we discuss the properties and limitations of three existing static pruning techniques with a focus on satisficing planning. We analyze these pruning techniques and their combinations and identify synergy effects between them and the domains and problem structures in which they occur. We implement the three methods into an existing propositional planner and evaluate the performance of different configurations and combinations in a set of experiments on IPC benchmarks. We observe that static pruning techniques can increase the number of solved problems and that the synergy effects of the combinations also occur on IPC benchmarks, although they do not lead to a major performance increase.

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