07612nam 22008655 450 991048481440332120200702220500.03-662-44923-410.1007/978-3-662-44923-3(CKB)3710000000249797(SSID)ssj0001354162(PQKBManifestationID)11896013(PQKBTitleCode)TC0001354162(PQKBWorkID)11323205(PQKB)10712093(DE-He213)978-3-662-44923-3(MiAaPQ)EBC6302180(MiAaPQ)EBC5594666(Au-PeEL)EBL5594666(OCoLC)892730704(PPN)181352095(EXLCZ)99371000000024979720140923d2014 u| 0engurnn#008mamaatxtccrInductive Logic Programming[electronic resource] 23rd International Conference, ILP 2013, Rio de Janeiro, Brazil, August 28-30, 2013, Revised Selected Papers /edited by Gerson Zaverucha, Vítor Santos Costa, Aline Paes1st ed. 2014.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2014.1 online resource (XIII, 141 p. 31 illus.)Lecture Notes in Artificial Intelligence ;8812Bibliographic Level Mode of Issuance: Monograph3-662-44922-6 Includes bibliographical references and index.Intro -- 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.A.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.This 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.Lecture Notes in Artificial Intelligence ;8812Mathematical logicArtificial intelligenceComputer programmingComputer logicComputersApplication softwareMathematical Logic and Formal Languageshttps://scigraph.springernature.com/ontologies/product-market-codes/I16048Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Programming Techniqueshttps://scigraph.springernature.com/ontologies/product-market-codes/I14010Logics and Meanings of Programshttps://scigraph.springernature.com/ontologies/product-market-codes/I1603XComputation by Abstract Deviceshttps://scigraph.springernature.com/ontologies/product-market-codes/I16013Information Systems Applications (incl. Internet)https://scigraph.springernature.com/ontologies/product-market-codes/I18040Mathematical logic.Artificial intelligence.Computer programming.Computer logic.Computers.Application software.Mathematical Logic and Formal Languages.Artificial Intelligence.Programming Techniques.Logics and Meanings of Programs.Computation by Abstract Devices.Information Systems Applications (incl. Internet).005.115Zaverucha Gersonedthttp://id.loc.gov/vocabulary/relators/edtSantos Costa Vítoredthttp://id.loc.gov/vocabulary/relators/edtPaes Alineedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK9910484814403321Inductive Logic Programming2804417UNINA