Inductive Logic Programming - Info and Reading Options

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Intro -- Preface -- Organization -- Contents -- CONNER: A Concurrent ILP Learner in Description Logic -- 1 Introduction -- 2 Background -- 3 Concurrent, GPU-Accelerated Cover Set Computation -- 4 Extending the Hypothesis Language -- 4.1 Cardinality Restriction Support -- 4.2 Data Property Restriction Support -- 5 CONNER: All Together Now -- 5.1 TBox Processing -- 5.2 Refinement Operator and Search Algorithm -- 5.3 Evaluation -- 6 Conclusion and Future Work -- References -- Towards Meta-interpretive Learning of Programming Language Semantics -- 1 Introduction -- 2 A Case Study -- 3 Overview of MetagolPLS -- 3.1 Function Variables in the Meta-rules -- 3.2 Non-terminating Examples -- 3.3 Non-observation Predicate and Multi-predicate Learning -- 4 Evaluation -- 5 Conclusion and Future Work -- References -- Towards an ILP Application in Machine Ethics -- 1 Introduction -- 2 Learning ASP Rules for Ethical Customer Service -- 3 Final Remarks and Future Directions -- References -- On the Relation Between Loss Functions and T-Norms -- 1 Introduction -- 2 Fuzzy Aggregation Functions -- 2.1 Archimedean T-Norms -- 3 From Formulas to Loss Functions -- 3.1 Loss Functions by T-Norms Generators -- 3.2 Redefinition of Supervised Learning with Logic -- 4 Experimental Results -- 5 Conclusions -- References -- Rapid Restart Hill Climbing for Learning Description Logic Concepts -- 1 Introduction -- 2 Related Work -- 3 Concept Learning in DL -- 3.1 The Concept Learning Problem -- 3.2 Refinement Operators -- 3.3 CELOE -- 4 Rapid Restart Hill Climbing (RRHC) -- 5 Experiments -- 5.1 Results and Discussions -- 6 Conclusion and Future Work -- References -- Neural Networks for Relational Data -- 1 Introduction -- 2 Related Work -- 3 Neural Networks with Relational Parameter Tying -- 3.1 Generating Lifted Random Walks -- 3.2 Network Instantiation -- 4 Experiments

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