1. Introduction. Probabilistic Logic Programming (PLP) started in the early 90s with seminal works such as those of Dantsin (1991), Ng and Subrahmanian (1992), Poole (1993), and Sato (1995).. Since ...

Inductive logic programming (ILP) and machine learning together represent a powerful synthesis of symbolic reasoning and statistical inference. ILP focuses on deriving interpretable logic rules ...

This paper outlines an alternative empirical approach based on techniques from a subfield of machine learning known as Inductive Logic Programming (ILP). ILP algorithms, which learn relational ...

Modern search techniques either cannot efficiently incorporate human feedback to refine search results or cannot express structural or semantic properties of desired code. The key insight of our ...

Book Abstract: Although Inductive Logic Programming (ILP) is generally thought of as a research area at the intersection of machine learning and computational logic, Bergadano and Gunetti propose that ...