论文标题
SMT + ILP
SMT + ILP
论文作者
论文摘要
归纳逻辑编程（ILP）在AI中一直是一种深厚的影响力范式，在其理论和实施方面享有数十年的研究。作为逻辑编程和机器学习领域的自然后裔，它承认了背景知识的合并，这在可用专家的先验知识的领域中非常有用，并且可以导致更高的数据效率学习制度。尽管如此，在布尔变量上组成的Horn条款的局限性是非常严重的。现实世界中发生的许多现象是使用连续实体以及更普遍的离散和连续实体混合物的最佳特征。在这个立场论文中，我们通过利用满意度模型理论技术来激发归纳宣言编程的重新考虑。
Inductive logic programming (ILP) has been a deeply influential paradigm in AI, enjoying decades of research on its theory and implementations. As a natural descendent of the fields of logic programming and machine learning, it admits the incorporation of background knowledge, which can be very useful in domains where prior knowledge from experts is available and can lead to a more data-efficient learning regime. Be that as it may, the limitation to Horn clauses composed over Boolean variables is a very serious one. Many phenomena occurring in the real-world are best characterized using continuous entities, and more generally, mixtures of discrete and continuous entities. In this position paper, we motivate a reconsideration of inductive declarative programming by leveraging satisfiability modulo theory technology.
