论文标题
关于越来越不表现力的逻辑程序的配置
On the Configuration of More and Less Expressive Logic Programs
论文作者
论文摘要
某个问题的表示形式（即其知识模型）与推理方面的脱钩是基于模型的人工智能（AI）的主要强度之一。这允许，例如专注于通过在整个解决过程中具有优势来改善推理方面。此外，还众所周知，许多求解器对输入的句法变化也非常敏感。在本文中，我们专注于通过这种敏感性的优势来改善推理方面。我们考虑了两个基于模型的AI方法，即SAT和ASP，它们定义了许多可能表征其输入的句法特征，并使用自动配置工具来重新制定输入公式或程序。涉及SAT和ASP领域的广泛实验分析的结果，从各自的竞争中获取，表明可以通过使用输入重新配置和配置获得不同的优势。在逻辑编程（TPLP）的理论和实践中考虑的。
The decoupling between the representation of a certain problem, i.e., its knowledge model, and the reasoning side is one of main strong points of model-based Artificial Intelligence (AI). This allows, e.g. to focus on improving the reasoning side by having advantages on the whole solving process. Further, it is also well-known that many solvers are very sensitive to even syntactic changes in the input. In this paper, we focus on improving the reasoning side by taking advantages of such sensitivity. We consider two well-known model-based AI methodologies, SAT and ASP, define a number of syntactic features that may characterise their inputs, and use automated configuration tools to reformulate the input formula or program. Results of a wide experimental analysis involving SAT and ASP domains, taken from respective competitions, show the different advantages that can be obtained by using input reformulation and configuration. Under consideration in Theory and Practice of Logic Programming (TPLP).
