This paper contributes to the area of inductive logic programming by presenting a new learning framework that allows the learning of weak constraints in Answer Set Programming (ASP). The framework, called Learning from Ordered Answer Sets, generalises our previous work on learning ASP programs without weak constraints, by considering a new notion of examples as ordered pairs of partial answer sets that exemplify which answer sets of a learned hypothesis (together with a given background knowledge) are preferred to others. In this new learning task inductive solutions are searched within a hypothesis space of normal rules, choice rules, and hard and weak constraints. We propose a new algorithm, ILASP2, which is sound and complete with respect to our new learning framework. We investigate its applicability to learning preferences in an interview scheduling problem and also demonstrate that when restricted to the task of learning ASP programs without weak constraints, ILASP2 can be much more efficient than our previously proposed system.

本文提出了一种新的学习框架，“从有序答案集学习”，它允许在答案集编程（ASP）中学习弱约束条件，它是在归纳逻辑编程领域的一项贡献，我们提出了一种新的算法ILASP2，它基于正常规则、选择规则、硬约束和弱约束等假设，能够有效地学习ASP程序，尤其是在学习ASP没有弱约束条件的程序时，相对于之前的方法能够更加高效。

学习Answer Set Programming中的弱约束