Stephen H. Muggleton and Hiroaki Watanabe (Eds.): Latest advances in inductive logic programming

Abstract

Inductive logic programming (ILP) investigates the construction of logic programs from training examples and background knowledge, where a logic program is a set of sentences in logical form, expressing facts and rules about some domain. ILP is more powerful than traditional machine learning methods, such as C4.5 decision trees, because it uses an expressive first-order logic framework and readily allows the user to incorporate background knowledge. In first-order logic, sentences are formed by using constants, variables, predicates, functions, quantifiers, and connectives in a well-formed format, where constants are the objects in the domain of discourse, variables range over the objects in the domain, predicates specify the properties of objects or the relations among objects, functions map object(s) onto other object(s), quantifiers are 8 (Universal) and 9 (Existential) that quantify the variables, and connectives include negation, and, or, etc. ILP has a strong theoretical foundation from logic programming and computational learning theory. There has been significant progress in ILP in the last two decades and this book contains 25 papers associated with the 21st International Conference of Inductive Logic Programming which was held in Cumberland Lodge, an educational charity and a unique conference center in the heart of the Great Park, Windsor, United Kingdom. The book is divided into 7 parts and the first part has 9 papers about applications of ILP in different domains. In Part 2 five research studies that extend ILP to probabilistic logical learning are presented. Part 3 contains a number of papers about the implementations of ILP systems. Research studies about theory, logical learning, constraints, as well as spatial and temporal learning are presented in parts 4–7.