Inductive Logic Programming - Info and Reading Options

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Intro -- Preface -- Organization -- Contents -- Reframing on Relational Data -- 1 Introduction -- 2 Related Work -- 3 Our Proposed Approach -- 3.1 Reframing Aggregate Condition -- 3.2 Reframing Aggregate Output -- 3.3 RASHC: Our Proposed Algorithm -- 4 Experimental Results -- 4.1 Performance in Synthetic Datasets -- 4.2 Performance in a Real-Life Dataset with Artificial Shifts -- 4.3 Performance in a Real-Life Dataset with Real Shifts -- 5 Conclusions -- References -- Inductive Learning Using Constraint-Driven Bias -- 1 Introduction -- 2 Background -- 3 Learning Using Constraint-Driven Bias -- 4 Implementation -- 5 Case Studies -- 5.1 Requirements Engineering -- 5.2 Hypothesis Stratification -- 6 Conclusion and Related Work -- References -- Nonmonotonic Learning in Large Biological Networks -- 1 Introduction -- 2 Metabolic Network Modelling -- 2.1 The AAA network -- 2.2 The ABER network -- 2.3 A Declarative Model of Metabolism -- 3 Metabolic Network Revision -- 3.1 ILP -- 3.2 Progol5 -- 3.3 XHAIL -- 4 Case Study -- 4.1 Making ABER consistent with AAA -- 4.2 Making ABER consistent with Robot Scientist data -- 5 Comparison with Progol5 -- 6 Conclusions -- References -- Construction of Complex Aggregates with Random Restart Hill-Climbing -- 1 Introduction -- 2 Preliminaries -- 2.1 Notations -- 2.2 Example: Urban Blocks -- 3 Related Work -- 4 Hill-Climbing of Complex Aggregate Conditions -- 5 Experiments and Results -- 5.1 Experiments on Artificial Data -- 5.2 Experiments on Real-World Data -- 6 Conclusion -- References -- Logical Minimisation of Meta-Rules Within Meta-Interpretive Learning -- 1 Introduction -- 1.1 Motivation -- 2 Related Work -- 3 Meta-Rules and Encapsulation -- 3.1 Specificational and Derivational Meta-Rules -- 3.2 Encapsulation -- 4 Logically Reducing Meta-Rules -- 4.1 Reduction of Pure Dyadic Meta-Rules in Hi=22

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