Inductive Logic Programming: 32nd International Conference, ILP 2023, Bari, Italy, November 13–15, 2023, Proceedings (Lecture Notes in Artificial Intelligence)

This book constitutes the refereed proceedings of the 32nd International Conference on Inductive Logic Programming, ILP 2023, held in Bari, Italy, during November 13–15, 2023. The 11 full papers and 1 short paper included in this book were carefully reviewed and selected from 18 submissions. They cover all aspects of learning in logic, multi-relational data mining, statistical relational learning, graph and tree mining, learning in other (non-propositional) logic-based knowledge representation frameworks, exploring intersections to statistical learning and other probabilistic approaches. Preface Organization Invited Talks Declarative Sequential Pattern Mining in ASP Extracting Rules from Machine Learning Models in Angluin’s Style Contents A Constrained Optimization Approach to Set the Parameters of Probabilistic Answer Set Programs 1 Introduction 2 Background 2.1 Probabilistic Optimizable Logic Programs 2.2 Related Work 3 Probabilistic Optimizable Answer Set Programs 3.1 Algorithm 4 Experiments 5 Conclusions References Regularization in Probabilistic Inductive Logic Programming 1 Introduction 2 Background 3 LIFTCOVER+ 4 Experiments 5 Related Work 6 Conclusions References Towards ILP-Based LTLf Passive Learning 1 Introduction 2 Background 2.1 Linear Temporal Logic over Finite Traces 2.2 Passive Learning of LTLf Formulae 2.3 Answer Set Programming 2.4 Learning from Answer Sets 3 Formalizing LTLf Semantics in ASP 4 LTLf Passive Learning in Plain ASP 5 LTLf Passive Learning Using ILASP 6 Evaluation 7 Related Works 8 Conclusion References Learning Strategies of Inductive Logic Programming Using Reinforcement Learning 1 Introduction 2 Related Work 2.1 Learning Strategies of ILP Systems 2.2 Meta Learning in ILP 3 Preliminary 3.1 Inductive Logic Programming 3.2 Popper 4 RL-Popper 4.1 Search Strategy 4.2 Learning Setting of RL 5 Experiments 5.1 Experimental Setting 5.2 Results 5.3 Discussion 6 Conclusion References Select First, Transfer Later: Choosing Proper Datasets for Statistical Relational Transfer Learning 1 Introduction 2 Background 2.1 Transfer Learning 2.2 Bottom Clause Propositionalization 3 Related Work 4 Finding from Where to Transfer with a Domain Similarity-Based Approach 5 Experimental Results 6 Conclusion References GNN Based Extraction of Minimal Unsatisfiable Subsets 1 Introduction 2 Related Work 2.1 Neuro-Based SAT Solvers 2.2 Minimal Unsatisfiable Subsets 3 Background 3.1 SAT Problem 3.2 MUS Extraction 3.3 SAT Solving with GNNs and GATs 4 MUS Prediction with GNNs 4.1 GNN-MUS 4.2 GNN Based Clause Set Trimming 4.3 MUS Extraction Algorithm Based on GNN-MUS 5 Experiments 5.1 Dataset 5.2 Training 5.3 Comparison of MUSX and NeuroMUSX 5.4 Comparison with Specifically Trained NeuroMUSX 5.5 Comparison of GNN-MUS and NeuroSAT 5.6 Discussion 6 Conclusion References What Do Counterfactuals Say About the World? Reconstructing Probabilistic Logic Programs from Answers to ``What If?'' Queries 1 Introduction 2 Preliminaries 3 Results 4 Conclusion References Few-Shot Learning of Diagnostic Rules for Neurodegenerative Diseases Using Inductive Logic Programming 1 Introduction 2 Few-Shot Learning 3 Feature Extraction for Neurodegenerative Disease Detection from Retinal Images 3.1 Feature Extraction from Retinal Images 4 Histogram-Based Binning Method 5 Empirical Evaluation 5.1 Materials 5.2 Methods 5.3 Results and Discussions 6 Conclusions References An Experimental Overview of Neural-Symbolic Systems 1 Introduction 2 Background 2.1 Regularization-Based Approaches 2.2 Logic Programming-Based Approaches 3 Related Work 4 Tasks 5 Experimental Evaluation 5.1 Tasks 5.2 Methodology 5.3 Results 5.4 Discussion 6 Conclusion References Statistical Relational Structure Learning with Scaled Weight Parameters 1 Introduction 2 Preliminaries 2.1 Markov Logic Networks 2.2 Domain-Size Aware Markov Logic Networks 2.3 Domain-Size Aware Relational Logistic Regression 2.4 Functional Gradient Boosting 3 Learning from Random Samples 3.1 Methods 3.2 Results 3.3 Discussion 4 Domain-Size Aware Markov Logic Networks with Respect to Changing Domain Structures 4.1 Methods 4.2 Results 4.3 Discussion 5 Conclusion References A Review of Inductive Logic Programming Applications for Robotic Systems 1 Introduction 2 A Robotic System 3 Inductive Logic Programming 4 Review 4.1 Perception 4.2 Knowledge Generation and Decision-Making 4.3 Potentials of ILP in Robotics 4.4 Limitations and Challenges 5 Conclusion References Meta-interpretive Learning from Fractal Images 1 Introduction 2 Related Work 3 Methodology 3.1 Feature Extraction 3.2 Dataset and Background Knowledge 3.3 Meta-interpretive Learning (MIL) 3.4 Comparing with Neural Networks 4 Experiments 5 Conclusions References Author Index