This book represents a selection of papers presented at the Inductive Logic Programming (ILP) workshop held at Cumberland Lodge, Great Windsor Park. The collection marks two decades since the first ILP workshop in 1991. During this period the area has developed into the main forum for work on logic-based machine learning. The chapters cover a wide variety of topics, ranging from theory and ILP implementations to state-of-the-art applications in real-world domains. The international contributors represent leaders in the field from prestigious institutions in Europe, North America and Asia.Graduate students and researchers in this field will find this book highly useful as it provides an up-to-date insight into the key sub-areas of implementation and theory of ILP. For academics and researchers in the field of artificial intelligence and natural sciences, the book demonstrates how ILP is being used in areas as diverse as the learning of game strategies, robotics, natural language understanding, query search, drug design and protein modelling.

This book constitutes the thoroughly refereed proceedings of the 8th Joint International Semantic Technology Conference, JIST 2018, held in Awaji, Japan, in November 2018. The 23 full papers and 6 short papers presented were carefully reviewed and selected from 75 submissions. They present applications of semantic technologies, theoretical results, new algorithms and tools to facilitate the adoption of semantic technologies and are organized in topical sections on knowledge graphs; data management; question answering and NLP; ontology and reasoning; government open data; and semantic web for life sciences.

This book constitutes the thoroughly refereed post-proceedings of the 21st International Conference on Inductive Logic Programming, ILP 2011, held in Windsor Great Park, UK, in July/August 2011. The 24 revised full papers were carefully reviewed and selected from 66 submissions. Also included are five extended abstracts and three invited talks. The papers represent the diversity and vitality in present ILP research including ILP theory, implementations, probabilistic ILP, biological applications, sub-group discovery, grammatical inference, relational kernels, learning of Petri nets, spatial learning, graph-based learning, and learning of action models.

This book constitutes the thoroughly refereed post-proceedings of the 16th International Conference on Inductive Logic Programming, ILP 2006, held in Santiago de Compostela, Spain, in August 2006. The papers address all current topics in inductive logic programming, ranging from theoretical and methodological issues to advanced applications.

This is the fifteenth volume in the Machine Intelligence series, founded in 1965 by Donald Michie, and includes papers by a number of eminent AI figures including John McCarthy, Alan Robinson, Robert Kowalski and Mike Genesereth. The book is centred on the theme of intelligent agents and covers a wide range of topics, including: - Representations of consciousness (John McCarthy, Stanford University and Donald Michie, Edinburgh University) - SoftBots (Bruce Blumberg, MIT Media Lab) - Parallel implementations of logic (Alan Robinson, Syracuse University) - Machine learning (Stephen Muggleton, Oxford University) - Machine vision (Andrew Blake, Oxford University) - Machine-based scientific discovery in molecular biology (Mike Sternberg, Imperial Cancer Research Fund).

In recent years there has been increasing excitement concerning the potential of Artificial Intelligence to transform human society. This book addresses the leading edge of research in this area. The research described aims to address present incompatibilities of Human and Machine reasoning and learning approaches. According to the influential US funding agency DARPA (originator of the Internet and Self-Driving Cars) this new area represents the Third Wave of Artificial Intelligence (3AI, 2020s-2030s), and is being actively investigated in the US, Europe and China. The chapters of this book have been authored by a mixture of UK and other international specialists. Some of the key questions addressed by the Human-Like Computing programme include how AI systems might 1) explain their decisions effectively, 2) interact with human beings in natural language, 3) learn from small numbers of examples and 4) learn with minimal supervision. Solving such fundamental problems involves new foundational research in both the Psychology of perception and interaction as well as the development of novel algorithmic approaches in Artificial Intelligence.