LEADER 07585nam 22008655 450 001 9910484814403321 005 20200702220500.0 010 $a3-662-44923-4 024 7 $a10.1007/978-3-662-44923-3 035 $a(CKB)3710000000249797 035 $a(SSID)ssj0001354162 035 $a(PQKBManifestationID)11896013 035 $a(PQKBTitleCode)TC0001354162 035 $a(PQKBWorkID)11323205 035 $a(PQKB)10712093 035 $a(DE-He213)978-3-662-44923-3 035 $a(MiAaPQ)EBC6302180 035 $a(MiAaPQ)EBC5594666 035 $a(Au-PeEL)EBL5594666 035 $a(OCoLC)892730704 035 $a(PPN)181352095 035 $a(EXLCZ)993710000000249797 100 $a20140923d2014 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aInductive Logic Programming $e23rd International Conference, ILP 2013, Rio de Janeiro, Brazil, August 28-30, 2013, Revised Selected Papers /$fedited by Gerson Zaverucha, Vítor Santos Costa, Aline Paes 205 $a1st ed. 2014. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2014. 215 $a1 online resource (XIII, 141 p. 31 illus.) 225 1 $aLecture Notes in Artificial Intelligence ;$v8812 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-662-44922-6 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- Organization -- Contents -- MetaBayes: Bayesian Meta-Interpretative Learning Using Higher-Order Stochastic Refinement -- 1 Introduction -- 1.1 Bayesian MIL Versus Probabilistic ILP -- 1.2 Multiple and Single Models -- 2 MetaBayes Refinement Framework -- 2.1 Setting -- 2.2 Generalised Meta-Interpreter -- 2.3 Stochastic Refinement -- 2.4 Prior, Likelihood and Posterior -- 3 Implementation -- 3.1 MetaBayes -- 3.2 MetaBayesMAP -- 3.3 MetaBayesSiLP -- 3.4 MilProbLog -- 4 Experiments -- 4.1 Binary Prediction - MetaBayes vs. MetaMap -- 4.2 Probabilistic Prediction - MetaBayes vs. MetaBayesSiLP vs. MilProbLog -- 5 Related Work -- 6 Conclusion and Further Work -- References -- On Differentially Private Inductive Logic Programming -- 1 Introduction -- 2 Preliminaries -- 2.1 Inductive Logic Programming -- 2.2 Differential Privacy -- 3 Problem Formulation -- 4 Trade-Off on Privacy and Utility -- 4.1 Our Utility Model -- 4.2 A Lower Bound on Privacy Parameter -- 5 Differentially Private ILP Algorithm -- 5.1 A Non-private ILP Algorithm -- 5.2 A Differentially Private Selection Algorithm -- 5.3 A Differentially Private Reduction Algorithm -- 5.4 Our Differentially Private ILP Algorithm -- 6 Experiment -- 7 Conclusion -- References -- Learning Through Hypothesis Refinement Using Answer Set Programming -- 1 Introduction -- 2 Background -- 2.1 Top-Directed Abductive Learning in ASP -- 3 Learning Through Hypothesis Refinement -- 3.1 Hypothesis Refinement -- 3.2 Learning a Partial Hypothesis -- 4 RASPAL: Iterative Learning by Refinement -- 4.1 Algorithms -- 5 Experiment -- 6 Conclusion and Future Work -- References -- A BDD-Based Algorithm for Learning from Interpretation Transition -- 1 Introduction -- 2 Learning from 1-Step Transitions -- 3 BDD Algorithms for LF1T -- 4 Experiments -- 5 Conclusion and Future Work -- A Appendix. 327 $aA.1 Proof of Theorem 1 -- References -- Accelerating Imitation Learning in Relational Domains via Transfer by Initialization -- 1 Introduction -- 2 Background -- 3 Relational Imitation Learning -- 4 Relational Transfer -- 5 Experiments -- 6 Discussion and Conclusion -- References -- A Direct Policy-Search Algorithm for Relational Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Terminology -- 3.1 Blocks World -- 4 CERRLA Algorithm -- 4.1 Cross-Entropy Method -- 4.2 Rule Discovery -- 4.3 Policy-Search Process -- 5 Evaluation -- 5.1 Blocks World -- 5.2 Ms.Pac-Man -- 5.3 Carcassonne -- 6 Conclusions -- References -- AND Parallelism for ILP: The APIS System -- 1 Introduction -- 2 Background -- 2.1 Parallel Execution of Logic Programs -- 3 The APIS System -- 3.1 Redundancy Avoidance -- 4 Experiments and Results -- 4.1 Experimental Settings -- 4.2 Results and Discussion -- 5 Parallel Execution of ILP Systems -- 6 Conclusions -- References -- Generalized Counting for Lifted Variable Elimination -- 1 Introduction -- 2 Representation -- 3 Lifted Variable Elimination -- 4 Generalized Counting Formulas -- 4.1 A Motivating Example -- 4.2 Definition -- 5 Conversion Operations -- 5.1 Counting Conversion -- 5.2 Merging Counting Formulas -- 5.3 Merge-Counting -- 6 Elimination Operations -- 6.1 Sum-out by Counting -- 6.2 Aggregation -- 7 Relation to Joint Conversion -- 8 Conclusion -- References -- A FOIL-Like Method for Learning under Incompleteness and Vagueness -- 1 Introduction -- 2 Preliminaries -- 3 Learning Fuzzy EL(D) Axioms -- 3.1 The Problem Statement -- 3.2 The Solution Strategy -- 4 Related Work -- 5 Towards an Application in Tourism -- 6 Conclusions -- References -- Author Index. 330 $aThis book constitutes the thoroughly refereed post-proceedings of the 23rd International Conference on Inductive Logic Programming, ILP 2013, held in Rio de Janeiro, Brazil, in August 2013. The 9 revised extended papers were carefully reviewed and selected from 42 submissions. The conference now focuses on all aspects of learning in logic, multi-relational learning and data mining, statistical relational learning, graph and tree mining, relational reinforcement learning, and other forms of learning from structured data. 410 0$aLecture Notes in Artificial Intelligence ;$v8812 606 $aMathematical logic 606 $aArtificial intelligence 606 $aComputer programming 606 $aComputer logic 606 $aComputers 606 $aApplication software 606 $aMathematical Logic and Formal Languages$3https://scigraph.springernature.com/ontologies/product-market-codes/I16048 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aProgramming Techniques$3https://scigraph.springernature.com/ontologies/product-market-codes/I14010 606 $aLogics and Meanings of Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/I1603X 606 $aComputation by Abstract Devices$3https://scigraph.springernature.com/ontologies/product-market-codes/I16013 606 $aInformation Systems Applications (incl. Internet)$3https://scigraph.springernature.com/ontologies/product-market-codes/I18040 615 0$aMathematical logic. 615 0$aArtificial intelligence. 615 0$aComputer programming. 615 0$aComputer logic. 615 0$aComputers. 615 0$aApplication software. 615 14$aMathematical Logic and Formal Languages. 615 24$aArtificial Intelligence. 615 24$aProgramming Techniques. 615 24$aLogics and Meanings of Programs. 615 24$aComputation by Abstract Devices. 615 24$aInformation Systems Applications (incl. Internet). 676 $a005.115 702 $aZaverucha$b Gerson$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSantos Costa$b Vítor$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPaes$b Aline$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484814403321 996 $aInductive Logic Programming$92804417 997 $aUNINA