LEADER 04105nam 22007215 450 001 9910349412703321 005 20251225202031.0 010 $a9783319999609 010 $a3319999605 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 $e28th International Conference, ILP 2018, Ferrara, Italy, September 2?4, 2018, Proceedings /$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 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v11105 311 08$a9783319999593 311 08$a3319999591 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,$x2945-9141 ;$v11105 606 $aArtificial intelligence 606 $aComputer science 606 $aCompilers (Computer programs) 606 $aComputer programming 606 $aInformation technology$xManagement 606 $aArtificial Intelligence 606 $aComputer Science Logic and Foundations of Programming 606 $aCompilers and Interpreters 606 $aProgramming Techniques 606 $aComputer Application in Administrative Data Processing 615 0$aArtificial intelligence. 615 0$aComputer science. 615 0$aCompilers (Computer programs) 615 0$aComputer programming. 615 0$aInformation technology$xManagement. 615 14$aArtificial Intelligence. 615 24$aComputer Science Logic and Foundations of Programming. 615 24$aCompilers and Interpreters. 615 24$aProgramming Techniques. 615 24$aComputer Application 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 $a9910349412703321 996 $aInductive Logic Programming$92804417 997 $aUNINA