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Inductive Logic Programming
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.
Fungi in Extreme Environments: Ecological Role and Biotechnological Significance
Over the last decades, scientists have been intrigued by the fascinating organisms that inhabit extreme environments. These organisms, known as extremophiles, thrive in habitats which for other terrestrial life-forms are intolerably hostile or even lethal. Based on such technological advances, the study of extremophiles has provided, over the last few years, ground-breaking discoveries that challenge the paradigms of modern biology. In the new bioeconomy, fungi in general, play a very important role in addressing major global challenges, being instrumental for improved resource efficiency, making renewable substitutes for products from fossil resources, upgrading waste streams to valuable fo...
Inductive Logic Programming
This book constitutes the thoroughly refereed post-conference proceedings of the 25th International Conference on Inductive Logic Programming, ILP 2015, held in Kyoto, Japan, in August 2015. The 14 revised papers presented were carefully reviewed and selected from 44 submissions. The papers focus on topics such as theories, algorithms, representations and languages, systems and applications of ILP, and cover all areas of learning in logic, relational learning, relational data mining, statistical relational learning, multi-relational data mining, relational reinforcement learning, graph mining, connections with other learning paradigms, among others.
Latest Advances In Inductive Logic Programming
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.
Inductive Logic Programming
This book constitutes the proceedings of the 19th International Conference on Inductive Logic Programming, held in Leuven, Belgium, in July 2009.
Inductive Logic Programming
This book constitutes the refereed conference proceedings of the 30th International Conference on Inductive Logic Programming, ILP 2021, held in October 2021. Due to COVID-19 pandemic the conference was held virtually. The 16 papers and 3 short papers presented were carefully reviewed and selected from 19 submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.
Rules and Reasoning
This book constitutes the proceedings of the International Joint Conference on Rules and Reasoning, RuleML+RR 2022, held in Berlin, Germany, during September 26–28, 2022. This is the 6th conference of a new series, joining the efforts of two existing conference series, namely “RuleML” (International Web Rule Symposium) and “RR” (Web Reasoning and Rule Systems). The 18 full research papers presented in this book were carefully reviewed and selected from 54 submissions. The papers cover the following topics: answer set programming; foundations of nonmonotonic reasoning; datalog; queries over ontologies; proofs, error-tolerance, and rules; as well as agents and argumentation.
Inductive Logic Programming
This book constitutes the thoroughly refereed post-proceedings of the 20th International Conference on Inductive Logic Programming, ILP 2010, held in Florence, Italy in June 2010. The 11 revised full papers and 15 revised short papers presented together with abstracts of three invited talks were carefully reviewed and selected during two rounds of refereeing and revision. All current issues in inductive logic programming, i.e. in logic programming for machine learning are addressed, in particular statistical learning and other probabilistic approaches to machine learning are reflected.
Efficient Frequent Subtree Mining Beyond Forests
A common paradigm in distance-based learning is to embed the instance space into a feature space equipped with a metric and define the dissimilarity between instances by the distance of their images in the feature space. Frequent connected subgraphs are sometimes used to define such feature spaces if the instances are graphs, but identifying the set of frequent connected subgraphs and subsequently computing embeddings for graph instances is computationally intractable. As a result, existing frequent subgraph mining algorithms either restrict the structural complexity of the instance graphs or require exponential delay between the output of subsequent patterns, meaning that distance-based lea...
Inductive Logic Programming
Mich`eleSebag(EcolePolytechnique,France) AshwinSrinivasan(UniversityofOxford,UK) PrasadTadepalli(OregonStateUniversity,USA) StefanWrobel(UniversityofMagdeburg,Germany) AkihiroYamamoto(UniversityofHokkaido,Japan) Additional Referees ́ ErickAlphonse(Universit ́edeParis-Sud,France) LiviuBadea(NationalInstituteforResearchandDevelopmentinInformatics,
