Fast Plan Adaptation through Planning Graphs: Local and Systematic Search Techniques

Alfonso Gerevini and Ivan Serina

Fast plan adaptation is important in many AI applications. From a theoretical point of view, in the worst case adapting an existing plan to solve a new problem is no more efficient than a complete regeneration of the plan. However, in practice plan adaptation can be much more efficient than plan generation, especially when the adapted plan can be obtained by performing a limited amount ofchanges to the original plan. In this paper we propose a domain-independent method for plan adaptation that combines two techniques. The first technique modifies the original plan by replanning within limited temporal windows containing portions of the plan that need to be revised. Each window is associated with a particular replanning subproblem that is solved using systematic search for Planning Graphs. The second technique modifies the original plan using local search for Action Graphs, which are particular subgraphs of a planning graph. This technique can be used either for solving a plan adaptation task or as a preprocessing for reducing the number of inconsistencies in the input plan. Experimental results show that in practice adapting a plan using our techniques can be very efficient.


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