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Foundations of Probabilistic Logic Programming
Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. This book aims at providing an overview of the field with a special emphasis on languages under the Distribution Semantics, one of the most influential approaches. The book presents the main ideas for semantics, inference, and learning and highlights connections between the methods. Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online. This 2nd edition aims at reporting the most exciting novelties in the field since the publication of the 1st edi...
Probabilistic Semantic Web
The management of uncertainty in the Semantic Web is of foremost importance given the nature and origin of the available data. This book presents a probabilistic semantics for knowledge bases, DISPONTE, which is inspired by the distribution semantics of Probabilistic Logic Programming. The book also describes approaches for inference and learning. In particular, it discusses 3 reasoners and 2 learning algorithms. BUNDLE and TRILL are able to find explanations for queries and compute their probability with regard to DISPONTE KBs while TRILLP compactly represents explanations using a Boolean formula and computes the probability of queries. The system EDGE learns the parameters of axioms of DISPONTE KBs. To reduce the computational cost, EDGEMR performs distributed parameter learning. LEAP learns both the structure and parameters of KBs, with LEAPMR using EDGEMR for reducing the computational cost. The algorithms provide effective techniques for dealing with uncertain KBs and have been widely tested on various datasets and compared with state of the art systems.
Machine Learning and Non-volatile Memories
This book presents the basics of both NAND flash storage and machine learning, detailing the storage problems the latter can help to solve. At a first sight, machine learning and non-volatile memories seem very far away from each other. Machine learning implies mathematics, algorithms and a lot of computation; non-volatile memories are solid-state devices used to store information, having the amazing capability of retaining the information even without power supply. This book will help the reader understand how these two worlds can work together, bringing a lot of value to each other. In particular, the book covers two main fields of application: analog neural networks (NNs) and solid-state ...
Machine Learning, Optimization, and Data Science
This two-volume set, LNCS 13163-13164, constitutes the refereed proceedings of the 7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021, together with the first edition of the Symposium on Artificial Intelligence and Neuroscience, ACAIN 2021. The total of 86 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 215 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, neuroscience, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.
Inductive Logic Programming
This book constitutes the refereed proceedings of the 32nd International Conference on Inductive Logic Programming, ILP 2023, held in Bari, Italy, during November 13–15, 2023. The 11 full papers and 1 short paper included in this book were carefully reviewed and selected from 18 submissions. They cover all aspects of learning in logic, multi-relational data mining, statistical relational learning, graph and tree mining, learning in other (non-propositional) logic-based knowledge representation frameworks, exploring intersections to statistical learning and other probabilistic approaches.
Knowledge Science, Engineering and Management
This book constitutes the proceedings of the 4th International Conference on Knowledge Science, Engineering and Management held in Belfast, Northern Ireland, UK, in September 2010.
Inductive Logic Programming
This book constitutes the refereed conference proceedings of the 28th International Conference on Inductive Logic Programming, ILP 2018, held in Ferrara, Italy, in September 2018. The 10 full papers presented were carefully reviewed and selected from numerous 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.
AIxIA 2021 – Advances in Artificial Intelligence
This book constitutes revised selected papers from the refereed proceedings of the 20th International Conference of the Italian Association for Artificial Intelligence, AIxIA 2021, which was held virtually in December 2021. The 36 full papers included in this book were carefully reviewed and selected from 58 submissions; the volume also contains 12 extended and revised workshop contributions. The papers were organized in topical sections as follows: Planning and strategies; constraints, argumentation, and logic programming; knowledge representation, reasoning, and learning; natural language processing; AI for content and social media analysis; signal processing: images, videos and speech; machine learning for argumentation, explanation, and exploration; machine learning and applications; and AI applications.
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.
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.
