04402nam 22007215 450 99641828380331620200703070814.03-030-49210-910.1007/978-3-030-49210-6(CKB)5280000000218578(MiAaPQ)EBC6297263(DE-He213)978-3-030-49210-6(PPN)248595083(EXLCZ)99528000000021857820200602d2020 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierInductive Logic Programming[electronic resource] 29th International Conference, ILP 2019, Plovdiv, Bulgaria, September 3–5, 2019, Proceedings /edited by Dimitar Kazakov, Can Erten1st ed. 2020.Cham :Springer International Publishing :Imprint: Springer,2020.1 online resource (154 pages)Lecture Notes in Artificial Intelligence ;117703-030-49209-5 CONNER: A Concurrent ILP Learner in Description Logic -- Towards Meta-interpretive Learning of Programming Language Semantics -- Towards an ILP Application in Machine Ethics -- On the Relation Between Loss Functions and T-Norms -- Rapid Restart Hill Climbing for Learning Description Logic Concepts -- Neural Networks for Relational Data -- Learning Logic Programs from Noisy State Transition Data -- A New Algorithm for Computing Least Generalization of a Set of Atoms -- LazyBum: Decision Tree Learning Using Lazy Propositionalization -- Weight Your Words: the Effect of Different Weighting Schemes on Wordification Performance -- Learning Probabilistic Logic Programs over Continuous Data.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.Lecture Notes in Artificial Intelligence ;11770Artificial intelligenceMathematical logicComputer logicProgramming languages (Electronic computers)Application softwareComputersArtificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Mathematical Logic and Formal Languageshttps://scigraph.springernature.com/ontologies/product-market-codes/I16048Logics and Meanings of Programshttps://scigraph.springernature.com/ontologies/product-market-codes/I1603XProgramming Languages, Compilers, Interpretershttps://scigraph.springernature.com/ontologies/product-market-codes/I14037Computer Applicationshttps://scigraph.springernature.com/ontologies/product-market-codes/I23001Information Systems and Communication Servicehttps://scigraph.springernature.com/ontologies/product-market-codes/I18008Artificial intelligence.Mathematical logic.Computer logic.Programming languages (Electronic computers).Application software.Computers.Artificial Intelligence.Mathematical Logic and Formal Languages.Logics and Meanings of Programs.Programming Languages, Compilers, Interpreters.Computer Applications.Information Systems and Communication Service.005.115Kazakov Dimitaredthttp://id.loc.gov/vocabulary/relators/edtErten Canedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK996418283803316Inductive Logic Programming772244UNISA