| Description |
1 online resource (154 pages) |
| Series |
Lecture Notes in Computer Science ; 11770 |
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LNCS Sublibrary: SL 7, Artificial Intelligence |
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Lecture notes in computer science ; 11770.
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LNCS sublibrary. SL 7, Artificial intelligence.
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| Contents |
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 |
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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 |
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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 |
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3 Neural Networks with Relational Parameter Tying -- 3.1 Generating Lifted Random Walks -- 3.2 Network Instantiation -- 4 Experiments -- 5 Conclusion and Future Work -- References -- Learning Logic Programs from Noisy State Transition Data -- 1 Introduction -- 2 Background -- 3 -LFIT -- 3.1 Rule Classification -- 3.2 Model -- 3.3 Generating Training Data -- 4 Experiments -- 4.1 Experimental Methods -- 4.2 Discussion -- 5 Related Work -- 6 Conclusion -- References -- A New Algorithm for Computing Least Generalization of a Set of Atoms -- 1 Introduction -- 2 Preliminaries |
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3 Computing Least Generalization by Anti-combination -- 3.1 Anti-unification Algorithm -- 3.2 Algorithms for Computing Least Generalization of a Set of Atoms -- 4 Experimental Evaluation -- 4.1 Generating Test Data -- 4.2 Experimental results -- 5 Discussion -- 6 Conclusion -- References -- LazyBum: Decision Tree Learning Using Lazy Propositionalization -- 1 Introduction -- 2 OneBM -- 3 LazyBum -- 3.1 The LazyBum Algorithm -- 3.2 LDT Extension Strategies -- 3.3 Special Cases -- 3.4 Comparison with Related Work -- 4 Evaluation -- 4.1 Methodology -- 4.2 Results -- 5 Conclusion -- References |
| Summary |
This book constitutes the refereed conference proceedings of the 29th International Conference on Inductive Logic Programming, ILP 2019, held in Plovdiv, Bulgaria, in September 2019. The 11 papers presented were carefully reviewed and selected from numerous submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data |
| Notes |
International conference proceedings |
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Weight Your Words: The Effect of Different Weighting Schemes on Wordification Performance |
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Includes author index |
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Print version record |
| Subject |
Logic programming -- Congresses
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Induction (Logic) -- Congresses
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Machine learning -- Congresses
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Induction (Logic)
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Logic programming
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Machine learning
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| Genre/Form |
proceedings (reports)
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Conference papers and proceedings
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Conference papers and proceedings.
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Actes de congrès.
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| Form |
Electronic book
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| Author |
Kazakov, Dimitar
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Erten, Can
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| ISBN |
9783030492106 |
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3030492109 |
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