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首页> 外文会议>International Conference on Artificial Intelligence Planning and Scheduling; 2000414-17; Breckenridge,CO(US) >Fast Plan Adaptation through Planning Graphs: Local and Systematic Search Techniques
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Fast Plan Adaptation through Planning Graphs: Local and Systematic Search Techniques

机译：通过计划图快速调整计划：本地和系统搜索技术

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摘要

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 of changes 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.
机译：快速计划调整在许多AI应用程序中都很重要。从理论上讲，在最坏的情况下，调整现有计划以解决新问题并不比完全重新生成计划更有效。但是，实际上，计划调整比计划生成要有效得多，尤其是当可以通过对原始计划进行有限数量的更改而获得调整后的计划时。在本文中，我们提出了一种结合两种技术的，与领域无关的计划自适应方法。第一种技术通过在包含需要修改的计划部分的有限时间窗口内进行重新计划来修改原始计划。每个窗口都与特定的重新计划子问题相关联，该子问题可以通过系统地搜索“计划图”来解决。第二种技术是通过对动作图进行局部搜索来修改原始计划，这些动作图是计划图的特定子图。此技术可以用于解决计划调整任务，也可以用作减少输入计划中不一致数量的预处理。实验结果表明，在实践中使用我们的技术调整计划可能非常有效。

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