LEADER 05222nam 22007095 450 001 9910484167203321 005 20251226193448.0 010 $a3-642-38812-4 024 7 $a10.1007/978-3-642-38812-5 035 $a(CKB)2560000000105665 035 $a(DE-He213)978-3-642-38812-5 035 $a(SSID)ssj0000936546 035 $a(PQKBManifestationID)11483583 035 $a(PQKBTitleCode)TC0000936546 035 $a(PQKBWorkID)10974618 035 $a(PQKB)10430047 035 $a(MiAaPQ)EBC3096896 035 $a(PPN)170492710 035 $a(EXLCZ)992560000000105665 100 $a20130605d2013 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aInductive Logic Programming $e22nd International Conference, ILP 2012, Dubrovnik, Croatia, September 16-18,2012, Revised Selected papers /$fedited by Fabrizio Riguzzi, Filip Zelezny 205 $a1st ed. 2013. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2013. 215 $a1 online resource (X, 273 p. 81 illus.) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v7842 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a3-642-38811-6 327 $aA Relational Approach to Tool-Use Learning in Robots -- A Refinement Operator for Inducing Threaded-Variable Clauses -- Propositionalisation of Continuous Attributes beyond Simple Aggregation -- Topic Models with Relational Features for Drug Design -- Pairwise Markov Logic -- Evaluating Inference Algorithms for the Prolog Factor Language -- Polynomial Time Pattern Matching Algorithm for Ordered Graph Patterns -- Fast Parameter Learning for Markov Logic Networks Using Bayes Nets -- Bounded Least General Generalization -- Itemset-Based Variable Construction in Multi-relational Supervised Learning -- A Declarative Modeling Language for Concept Learning in Description Logics -- Identifying Driver?s Cognitive Load Using Inductive Logic Programming -- Opening Doors: An Initial SRL Approach -- Probing the Space of Optimal Markov Logic Networks for Sequence Labeling -- What Kinds of Relational Features Are Useful for Statistical Learning? -- Learning Dishonesty -- Heuristic Inverse Subsumption in Full-Clausal Theories -- Learning Unordered Tree Contraction Patterns in Polynomial TimeA Relational Approach to Tool-Use Learning in Robots -- A Refinement Operator for Inducing Threaded-Variable Clauses -- Propositionalisation of Continuous Attributes beyond Simple Aggregation -- Topic Models with Relational Features for Drug Design -- Pairwise Markov Logic -- Evaluating Inference Algorithms for the Prolog Factor Language -- Polynomial Time Pattern Matching Algorithm for Ordered Graph Patterns -- Fast Parameter Learning for Markov Logic Networks Using Bayes Nets -- Bounded Least General Generalization -- Itemset-Based Variable Construction in Multi-relational Supervised Learning -- A Declarative Modeling Language for Concept Learning in Description Logics -- Identifying Driver?s Cognitive Load Using Inductive Logic Programming -- Opening Doors: An Initial SRL Approach -- Probing the Space of Optimal Markov Logic Networks for Sequence Labeling -- What Kinds of Relational Features Are Useful for StatisticalLearning?.-Learning Dishonesty.-Heuristic Inverse Subsumption in Full-Clausal Theories.-Learning Unordered Tree Contraction Patterns in Polynomial Time. 330 $aThis book constitutes the thoroughly refereed post-proceedings of the 22nd International Conference on Inductive Logic Programming, ILP 2012, held in Dubrovnik, Croatia, in September 2012. The 18 revised full papers were carefully reviewed and selected from 41 submissions. The papers cover the following topics: propositionalization, logical foundations, implementations, probabilistic ILP, applications in robotics and biology, grammatical inference, spatial learning and graph-based learning. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v7842 606 $aMachine theory 606 $aArtificial intelligence 606 $aComputer programming 606 $aComputer science 606 $aFormal Languages and Automata Theory 606 $aArtificial Intelligence 606 $aProgramming Techniques 606 $aComputer Science Logic and Foundations of Programming 606 $aTheory of Computation 606 $aComputer Science 615 0$aMachine theory. 615 0$aArtificial intelligence. 615 0$aComputer programming. 615 0$aComputer science. 615 14$aFormal Languages and Automata Theory. 615 24$aArtificial Intelligence. 615 24$aProgramming Techniques. 615 24$aComputer Science Logic and Foundations of Programming. 615 24$aTheory of Computation. 615 24$aComputer Science. 676 $a005.131 702 $aRiguzzi$b Fabrizio$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZelezny$b Filip$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910484167203321 996 $aInductive Logic Programming$92804417 997 $aUNINA