Answer Set Programming (ASP) is a declarative logic formalism that allows to encode computational problems via logic programs. Despite the declarative nature of the formalism, some advanced expertise is required, in general, for designing an ASP encoding that can be efficiently evaluated by an actual ASP system. A common way for trying to reduce the burden of manually tweaking an ASP program consists in automatically rewriting the input encoding according to suitable techniques, for producing alternative, yet semantically equivalent, ASP programs. However, rewriting does not always grant benefits in terms of performance; hence, proper means are needed for predicting their effects with this respect. In this paper we describe an approach based on Machine Learning (ML) to automatically decide whether to rewrite. In particular, given an ASP program and a set of input facts, our approach chooses whether and how to rewrite input rules based on a set of features measuring their structural properties and domain information. To this end, a Multilayer Perceptrons model has then been trained to guide the ASP grounder I-DLV on rewriting input rules. We report and discuss the results of an experimental evaluation over a prototypical implementation.

中文翻译：

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一种机器学习指导的 ASP 逻辑程序重写方法

答案集编程 (ASP) 是一种声明式逻辑形式主义，它允许通过逻辑程序对计算问题进行编码。尽管形式主义具有声明性质，但通常需要一些高级专业知识来设计可由实际 ASP 系统有效评估的 ASP 编码。尝试减少手动调整 ASP 程序负担的常用方法包括根据合适的技术自动重写输入编码，以生成替代但语义等效的 ASP 程序。然而，重写并不总能带来性能方面的好处。因此，需要适当的手段来预测它们在这方面的影响。在本文中，我们描述了一种基于机器学习 (ML) 的方法来自动决定是否重写。特别是，给定一个 ASP 程序和一组输入事实，我们的方法根据一组测量其结构属性和域信息的特征来选择是否以及如何重写输入规则。为此，我们训练了一个多层感知器模型来指导 ASP 基础 I-DLV 重写输入规则。我们报告并讨论了对原型实现的实验评估结果。