Latest Advances In Inductive Logic Programming

Latest Advances In Inductive Logic Programming

Stephen H Muggleton, Hiroaki Watanabe

$78.00

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Description

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 ILP
  • Provides an up-to-date insight into the key sub-areas of implementation and theory of ILP
  • The papers in this volume do not appear in conference proceedings elsewhere in the literature


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