LEADER 04413nam 22006975 450 001 996466344403316 005 20200703074953.0 010 $a3-319-99960-5 024 7 $a10.1007/978-3-319-99960-9 035 $a(CKB)4100000005958288 035 $a(DE-He213)978-3-319-99960-9 035 $a(MiAaPQ)EBC6286435 035 $a(PPN)229916481 035 $a(EXLCZ)994100000005958288 100 $a20180823d2018 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aInductive Logic Programming$b[electronic resource] $e28th International Conference, ILP 2018, Ferrara, Italy, September 2?4, 2018, Proceedings /$fedited by Fabrizio Riguzzi, Elena Bellodi, Riccardo Zese 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (IX, 173 p. 201 illus., 20 illus. in color.) 225 1 $aLecture Notes in Artificial Intelligence ;$v11105 311 $a3-319-99959-1 320 $aIncludes bibliographical references and index. 327 $aDerivation 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. 330 $aThis book constitutes the refereed conference proceedings of the 28th International Conference on Inductive Logic Programming, ILP 2018, held in Ferrara, Italy, in September 2018. The 10 full papers presented were carefully reviewed and selected from numerous submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data. 410 0$aLecture Notes in Artificial Intelligence ;$v11105 606 $aArtificial intelligence 606 $aComputer logic 606 $aProgramming languages (Electronic computers) 606 $aComputer programming 606 $aApplication software 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aLogics and Meanings of Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/I1603X 606 $aProgramming Languages, Compilers, Interpreters$3https://scigraph.springernature.com/ontologies/product-market-codes/I14037 606 $aProgramming Techniques$3https://scigraph.springernature.com/ontologies/product-market-codes/I14010 606 $aComputer Appl. in Administrative Data Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/I2301X 615 0$aArtificial intelligence. 615 0$aComputer logic. 615 0$aProgramming languages (Electronic computers). 615 0$aComputer programming. 615 0$aApplication software. 615 14$aArtificial Intelligence. 615 24$aLogics and Meanings of Programs. 615 24$aProgramming Languages, Compilers, Interpreters. 615 24$aProgramming Techniques. 615 24$aComputer Appl. in Administrative Data Processing. 676 $a005.115 702 $aRiguzzi$b Fabrizio$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBellodi$b Elena$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZese$b Riccardo$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466344403316 996 $aInductive Logic Programming$9772244 997 $aUNISA