论文标题
归纳学习答案集程序的ILASP系统
The ILASP system for Inductive Learning of Answer Set Programs
论文作者
论文摘要
归纳逻辑编程（ILP）的目标是学习一个程序，该程序在某些现有背景知识的背景下解释了一组示例。直到最近，关于ILP的大多数研究都针对学习序言。我们自己的ILASP系统会学习答案集程序，包括正常规则，选择规则以及严格和弱限制。学习这样的表现力程序会大大扩大ILP的适用性；例如，启用偏好学习，学习常识性知识，包括默认和例外，以及学习非确定性理论。在本文中，我们首先概述了ILASP的学习框架及其功能。接下来是对ILASP系统演变的全面摘要，呈现了每个版本的优势和劣势，并特别强调可伸缩性。
The goal of Inductive Logic Programming (ILP) is to learn a program that explains a set of examples in the context of some pre-existing background knowledge. Until recently, most research on ILP targeted learning Prolog programs. Our own ILASP system instead learns Answer Set Programs, including normal rules, choice rules and hard and weak constraints. Learning such expressive programs widens the applicability of ILP considerably; for example, enabling preference learning, learning common-sense knowledge, including defaults and exceptions, and learning non-deterministic theories. In this paper, we first give a general overview of ILASP's learning framework and its capabilities. This is followed by a comprehensive summary of the evolution of the ILASP system, presenting the strengths and weaknesses of each version, with a particular emphasis on scalability.
