Probabilistic Inductive Logic Programming

This book provides an introduction to probabilistic inductive logic programming. It places emphasis on the methods based on logic programming principles and covers formalisms and systems, implementations and applications, as well as theory.

Talks about Logic Programming, Uncertainty Reasoning and Machine Learning. This book includes definitions that circumscribe the area formed by extending Inductive Logic Programming to cases annotated with probability values. It investigates the approach of Learning from proofs and the issue of upgrading Fisher Kernels to Relational Fisher Kernels.

Probabilistic Logic Programming extends Logic Programming by enabling the representation of uncertain information. Probabilistic Logic Programming is at the intersection of two wider research fields: the integration of logic and probability and Probabilistic Programming. Logic enables the representation of complex relations among entities while probability theory is useful for model uncertainty over attributes and relations. Combining the two is a very active field of study. Probabilistic Programming extends programming languages with probabilistic primitives that can be used to write complex probabilistic models. Algorithms for the inference and learning tasks are then provided automatically by the system. Probabilistic Logic programming is at the same time a logic language, with its knowledge representation capabilities, and a Turing complete language, with its computation capabilities, thus providing the best of both worlds. 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. Foundations of Probabilistic Logic Programming 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 book constitutes the refereed conference proceedings of the 29th International Conference on Inductive Logic Programming, ILP 2019, held in Plovdiv, Bulgaria, in September 2019. The 11 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.

The question, how to combine probability and logic with learning, is getting an increased attention in several disciplines such as knowledge representation, reasoning about uncertainty, data mining, and machine learning simulateously. This results in the newly emerging subfield known under the names of statistical relational learning and probabilistic inductive logic programming. This book provides an introduction to the field with an emphasis on the methods based on logic programming principles. It is concerned with formalisms and systems, implementations and applications, as well as with the theory of probabilistic inductive logic programming. The 13 chapters of this state-of-the-art survey start with an introduction to probabilistic inductive logic programming; moreover the book presents a detailed overview of the most important probabilistic logic learning formalisms and systems such as relational sequence learning techniques, using kernels with logical representations, Markov logic, the PRISM system, CLP(BN), Bayesian logic programs, and the independent choice logic. The third part provides a detailed account of some show-case applications of probabilistic inductive logic programming. The final part touches upon some theoretical investigations and includes chapters on behavioural comparison of probabilistic logic programming representations and a model-theoretic expressivity analysis.

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 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. Contents:Applications:Can ILP Learn Complete and Correct Game Strategies? (Stephen H Muggleton and Changze Xu)Induction in Nonmonotonic Causal Theories for a Domestic Service Robot (Jianmin Ji and Xiaoping Chen)Using Ontologies in Semantic Data Mining with g-SEGS and Aleph (Anže Vavpetič and Nada Lavră)Improving Search Engine Query Expansion Techniques with ILP (José Carlos Almeida Santos and Manuel Fonseca de Sam Bento Ribeiro)ILP for Cosmetic Product Selection (Hiroyuki Nishiyama and Fumio Mizoguchi)Learning User Behaviours in Real Mobile Domains (Andreas Markitanis, Domenico Corapi, Alessandra Russo and Emil C Lupu)Discovering Ligands for TRP Ion Channels Using Formal Concept Analysis (Mahito Sugiyama, Kentaro Imajo, Keisuke Otaki and Akihiro Yamamoto)Predictive Learning in Two-Way Datasets (Beau Piccart, Hendrik Blockeel, Andy Georges and Lieven Eeckhout)Model of Double-Strand Break of DNA in Logic-Based Hypothesis Finding (Barthelemy Dworkin, Andrei Doncescu, Jean-Charles Faye and Katsumi Inoue)Probabilistic Logical Learning:The PITA System for Logical-Probabilistic Inference (Fabrizio Riguzzi and Terrance Swift)Learning a Generative Failure-Free PRISM Clause (Waleed Alsanie and James Cussens)Statistical Relational Learning of Object Affordances for Robotic Manipulation (Bogdan Moldovan, Martijn van Otterlo, Plinio Moreno, José Santos-Victor and Luc De Raedt)Learning from Linked Data by Markov Logic (Man Zhu and Zhiqiang Gao)Satisfiability Machines (Filip Železný)Implementations:Customisable Multi-Processor Acceleration of Inductive Logic Programming (Andreas K Fidjeland, Wayne Luk and Stephen H Muggleton)Multivalue Learning in ILP (Orlando Muoz Texzocotetla and Ren Mac Kinney Romero)Learning Dependent-Concepts in ILP: Application to Model-Driven Data Warehouses (Moez Essaidi, Aomar Osmani and Céline Rouveirol)Graph Contraction Pattern Matching for Graphs of Bounded Treewidth (Takashi Yamada and Takayoshi Shoudai)mLynx: Relational Mutual Information (Nicola Di Mauro, Teresa M A Basile, Stefano Ferilli and Floriana Esposito)Theory:Machine Learning Coalgebraic Proofs (Ekaterina Komendantskaya)Can ILP Deal with Incomplete and Vague Structured Knowledge? (Francesca A Lisi and Umberto Straccia)Logical Learning:Towards Efficient Higher-Order Logic Learning in a First-Order Datalog Framework (Niels Pahlavi and Stephen H Muggleton)Automatic Invention of Functional Abstractions (Robert J Henderson and Stephen H Muggleton)Constraints:Using Machine-Generated Soft Constraints for Roster Problems (Yoshihisa Shiina and Hayato Ohwada)Spatial and Temporal:Relational Learning for Football-Related Predictions (Jan Van Haaren and Guy Van den Broeck) Readership: Graduate students and researchers in the field of ILP, and academics and researchers in the fields of artificial intelligence and natural sciences. Key Features:Covers major areas of research in ILPProvides an up-to-date insight into the key sub-areas of implementation and theory of ILPThe papers in this volume do not appear in conference proceedings elsewhere in the literatureKeywords:Machine Learning;Logic Programs;Inductive Inference;Structure Learning;Relational Learning;Statistical Relational Learning

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.