LEADER 04915nam 22007575 450 001 9910552749203321 005 20251225212330.0 010 $a3-030-97454-5 024 7 $a10.1007/978-3-030-97454-1 035 $a(MiAaPQ)EBC6897066 035 $a(Au-PeEL)EBL6897066 035 $a(CKB)21383023100041 035 $a(PPN)260825565 035 $a(BIP)83380780 035 $a(BIP)83139329 035 $a(DE-He213)978-3-030-97454-1 035 $a(EXLCZ)9921383023100041 100 $a20220223d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aInductive Logic Programming $e30th International Conference, ILP 2021, Virtual Event, October 25?27, 2021, Proceedings /$fedited by Nikos Katzouris, Alexander Artikis 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (293 pages) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v13191 300 $aIncludes index. 311 08$aPrint version: Katzouris, Nikos Inductive Logic Programming Cham : Springer International Publishing AG,c2022 9783030974534 327 $aEmbedding Models for Knowledge Graphs Induced by Clusters of Relations and Background Knowledge -- Fanizzi Automatic Conjecturing of P-Recursions Using Lifted Inference -- Machine learning of microbial interactions using Abductive ILP and Hypothesis Frequency/Compression Estimation -- Answer-Set Programs for Reasoning about Counterfactual Interventions and Responsibility Scores for Classification -- Reyes Synthetic Datasets and Evaluation Tools for Inductive Neural Reasoning -- Using Domain-Knowledge to Assist Lead Discovery in Early-Stage Drug Design -- Non-Parametric Learning of Embeddings for Relational Data using Gaifman Locality Theorem -- Ontology Graph Embeddings and ILP for Financial Forecasting -- Transfer learning for boosted relational dependency networks through genetic algorithm -- Online Learning of Logic Based Neural Network Structures -- Programmatic policy extraction by iterative local search -- Mapping across relational domains for transfer learning with word embeddings-based similarity -- A First Step Towards Even More Sparse Encodings of Probability Distributions -- Feature Learning by Least Generalization -- Learning Logic Programs Using Neural Networks by Exploiting Symbolic Invariance -- Learning and revising dynamic temporal theories in the full Discrete Event Calculus -- Human-like rule learning from images using one-shot hypothesis derivation -- Generative Clausal Networks: Relational Decision Trees as Probabilistic Circuits -- A Simulated Annealing Meta-heuristic for Concept Learning in Description Logics. . 330 $aThis book constitutes the refereed conference proceedings of the 30th International Conference on Inductive Logic Programming, ILP 2021, held in October 2021. Due to COVID-19 pandemic the conference was held virtually. The 16 papers and 3 short papers presented were carefully reviewed and selected from 19 submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v13191 606 $aArtificial intelligence 606 $aComputer engineering 606 $aComputer networks 606 $aCompilers (Computer programs) 606 $aComputer science 606 $aMachine theory 606 $aArtificial Intelligence 606 $aComputer Engineering and Networks 606 $aCompilers and Interpreters 606 $aComputer Science Logic and Foundations of Programming 606 $aFormal Languages and Automata Theory 615 0$aArtificial intelligence. 615 0$aComputer engineering. 615 0$aComputer networks. 615 0$aCompilers (Computer programs) 615 0$aComputer science. 615 0$aMachine theory. 615 14$aArtificial Intelligence. 615 24$aComputer Engineering and Networks. 615 24$aCompilers and Interpreters. 615 24$aComputer Science Logic and Foundations of Programming. 615 24$aFormal Languages and Automata Theory. 676 $a005.115 676 $a005.115 702 $aKatzouris$b Nikos 702 $aArtikis$b Alexander 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910552749203321 996 $aInductive Logic Programming$92804417 997 $aUNINA