04413nam 22006975 450 99646634440331620200703074953.03-319-99960-510.1007/978-3-319-99960-9(CKB)4100000005958288(DE-He213)978-3-319-99960-9(MiAaPQ)EBC6286435(PPN)229916481(EXLCZ)99410000000595828820180823d2018 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierInductive Logic Programming[electronic resource] 28th International Conference, ILP 2018, Ferrara, Italy, September 2–4, 2018, Proceedings /edited by Fabrizio Riguzzi, Elena Bellodi, Riccardo Zese1st ed. 2018.Cham :Springer International Publishing :Imprint: Springer,2018.1 online resource (IX, 173 p. 201 illus., 20 illus. in color.) Lecture Notes in Artificial Intelligence ;111053-319-99959-1 Includes bibliographical references and index.Derivation reduction of metarules in meta-interpretive learning -- Large-Scale Assessment of Deep Relational Machines -- How much can experimental cost be reduced in active learning of agent strategies? -- Diagnostics of Trains with Semantic Diagnostics Rules -- The game of Bridge: a challenge for ILP -- Sampling-Based SAT/ASP Multi-Model Optimization as a Framework for Probabilistic Inference -- Explaining Black-box Classifiers with ILP - Empowering LIME with Aleph to Approximate Non-linear Decisions with Relational Rules -- Learning Dynamics with Synchronous, Asynchronous and General Semantics -- Was the Year 2000 a Leap Year? Step-wise Narrowing Theories with Metagol -- Targeted End-to-end Knowledge Graph Decomposition.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.Lecture Notes in Artificial Intelligence ;11105Artificial intelligenceComputer logicProgramming languages (Electronic computers)Computer programmingApplication softwareArtificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Logics and Meanings of Programshttps://scigraph.springernature.com/ontologies/product-market-codes/I1603XProgramming Languages, Compilers, Interpretershttps://scigraph.springernature.com/ontologies/product-market-codes/I14037Programming Techniqueshttps://scigraph.springernature.com/ontologies/product-market-codes/I14010Computer Appl. in Administrative Data Processinghttps://scigraph.springernature.com/ontologies/product-market-codes/I2301XArtificial intelligence.Computer logic.Programming languages (Electronic computers).Computer programming.Application software.Artificial Intelligence.Logics and Meanings of Programs.Programming Languages, Compilers, Interpreters.Programming Techniques.Computer Appl. in Administrative Data Processing.005.115Riguzzi Fabrizioedthttp://id.loc.gov/vocabulary/relators/edtBellodi Elenaedthttp://id.loc.gov/vocabulary/relators/edtZese Riccardoedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK996466344403316Inductive Logic Programming772244UNISA