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Schematic Invariants by Reduction to Ground Invariants

机译:通过减少到地面不变的原理图不变

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摘要

Computation of invariants, which are approximate reachability information for state-space search problems such as AI planning, has been considered to be more scalable when using a schematic representation of actions/events rather than an instantiated/ground representation. A disadvantage of schematic algorithms, however, is their complexity, which also leads to high runtimes when the number of schematic events/actions is high. We propose algorithms that reduce the problem of finding schematic invariants to solving a smaller ground problem.
机译:当使用操作/事件的示意图而不是实例化/地面表示时,诸如AI计划之类的状态空间搜索问题的不变性的不变量的计算是更可扩展的。 然而,示意性算法的缺点是它们的复杂性,这在原理图事件/动作的数量高时也会导致高的运行时间。 我们提出了降低解决较小地面问题的示意性不变性的问题的算法。

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