Automatic Instance Generation for Classical Planning
Automatic Instance Generation for Classical Planning
复制标题
经典规划的自动实例生成
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Silvan Sievers
中科院分区：
文献类型：
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作者：
Á. Torralba;Jendrik Seipp;Silvan Sievers
以前的国际规划竞赛（IPC）的基准是评估规划算法的事实上的标准。IPC集既是规划域的集合，也是从这些域中选择的实例。大多数域都带有一个参数化生成器，它为给定的一组参数值生成新的实例。由于规划研究的稳步发展，过去IPC产生的一些实例不足以评估当前的规划者。为了缓解这个问题，我们引入了Autoscale，这是一个为给定域选择实例的自动工具。Autoscale考虑到来自领域设计者的约束以及当前规划者的性能，以生成适当难度的实例集，同时避免对所考虑的规划者有太多的偏见。我们表明，由此产生的基准集是上级的IPC集，并有潜力提高规划研究的经验评价。
The benchmarks from previous International Planning Competitions (IPCs) are the de-facto standard for evaluating planning algorithms. The IPC set is both a collection of planning domains and a selection of instances from these domains. Most of the domains come with a parameterized generator that generates new instances for a given set of parameter values. Due to the steady progress of planning research some of the instances that were generated for past IPCs are inadequate for evaluating current planners. To alleviate this problem, we introduce Autoscale, an automatic tool that selects instances for a given domain. Autoscale takes into account constraints from the domain designer as well as the performance of current planners to generate an instance set of appropriate difficulty, while avoiding too much bias with respect to the considered planners. We show that the resulting benchmark set is superior to the IPC set and has the potential of improving empirical evaluation of planning research.