Recent work by Kautz et al. provides tantalizing evidence that large, classical planning problems may be efficiently solved by translating them into propositional satisfiability problems, using stochastic search techniques, and translating the resulting t r u t h assignments back into plans for the original problems. We explore the space of such transformations, providing a simple framework that generates eight major encodings (generated by selecting one of four action representations and one of two frame axioms) and a number of subsidiary ones. We describe a fully-implemented compiler that can generate each of these encodings, and we test the compiler on a suite of STRIPS planning problems in order to determine which encodings have the best properties.
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机译：Kautz等人的最新工作。通过将其转换为命题可息性问题，可以使用随机搜索技术将其转换为命题可满足问题，并将所得T r u t h分配转换为原始问题的计划，可以通过将其转换为诱人的典型规划问题。我们探讨了这种转换的空间，提供了一个简单的框架，它生成八个主要编码（通过选择四个动作表示之一和两个帧公理之一而生成）和多个子公司。我们描述了一个完全实现的编译器，可以生成这些编码，并且我们在一套条带计划问题上测试编译器，以确定哪些编码具有最佳属性。
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