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Inductive 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 Paes
Inductive 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 Paes
Edizione [1st ed. 2014.]
Pubbl/distr/stampa Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014
Descrizione fisica 1 online resource (XIII, 141 p. 31 illus.)
Disciplina 005.115
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Mathematical 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)
ISBN 3-662-44923-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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.
Record Nr. UNINA-9910484814403321
Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Inductive 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 Paes
Inductive 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 Paes
Edizione [1st ed. 2014.]
Pubbl/distr/stampa Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014
Descrizione fisica 1 online resource (XIII, 141 p. 31 illus.)
Disciplina 005.115
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Mathematical 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)
ISBN 3-662-44923-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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.
Record Nr. UNISA-996199679503316
Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui