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 ...

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 ...

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 ...

An Inductive Logic Programming-Based Approach for TV Stream Segment Classification Abstract: This paper proposes a method for classifying TV stream segments as long programs or inter-programs (IP). As ...

We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge base with a weight ...