Inductive Logic Programming: 31st International Conference, ILP 2022, Windsor Great Park, UK, September 28–30, 2022, Proceedings (Lecture Notes in Computer Science, 13779)

This book constitutes the refereed proceedings of the 31st International Conference on Inductive Logic Programming, ILP 2022, held during September 28-30, 2022. The 11 regular papers presented in this book were carefully reviewed and selected from 26 submissions The papers in these proceedings represent the diversity and vitality in present ILP research, including statistical relational learning, transfer learning, scientific reasoning, learning temporal models, synthesis and planning, and argumentation and language. Preface Organization Contents Learning the Parameters of Probabilistic Answer Set Programs 1 Introduction 2 Background 2.1 Answer Set Programming 2.2 Probabilistic Logic Programming 2.3 Credal Semantics 3 Parameter Learning in PASP 4 Experiments 5 Related Work 6 Conclusions References Navigable Atom-Rule Interactions in PSL Models Enhanced by Rule Verbalizations, with an Application to Etymological Inference 1 Introduction 2 Analyzing PSL Programs as Rule-Atom Graphs 2.1 Upward and Downward Pressure 2.2 Active and Inactive Rules 3 Verbalization of Atoms and Rules 4 Implementation 4.1 Infrastructure 4.2 Design of the RAG Viewer 4.3 Interface for Verbalizations 5 Application Example 5.1 Etymological Reasoning 5.2 Expressing Etymologies in PSL 5.3 The Etymological Inference Model 5.4 Exploring an Example Instance 6 Conclusion References A Program-Synthesis Challenge for ARC-Like Tasks 1 Introduction 2 Mathematical Morphology (MM) 3 Program Synthesis for ARC-Like Tasks 3.1 ARC-Like Images and Tasks 3.2 Functional Categories for ARC-Like Tasks 3.3 ILP-Based Program Synthesis for ARC-Like Tasks 4 IPARC 4.1 Category A: Warm-Up 4.2 Category B: Learning Sub-programs 4.3 Category C: Learning Programs from Traces 5 Concluding Remarks A Proofs References Explaining with Attribute-Based and Relational Near Misses: An Interpretable Approach to Distinguishing Facial Expressions of Pain and Disgust 1 Introduction 2 Contrastive Explanations with Near Misses 3 An Interpretable Approach to Explain Facial Expressions of Pain Versus Disgust 3.1 Learning from Symbolic Representations of Video Sequences 3.2 Selecting Attribute-Based and Relational Near Misses Based on Similarity Metrics 4 Evaluation 5 Discussion and Conclusion References Learning Automata-Based Complex Event Patterns in Answer Set Programming 1 Introduction 2 Related Work 3 ASP Background 4 The Problem Setting and a Running Example 5 Answer Set Automata 6 Learning Answer Set Automata 6.1 Incremental Learning & Automata Revision 7 Experimental Evaluation 8 Conclusions and Future Work References Learning Hierarchical Problem Networks for Knowledge-Based Planning 1 Introduction 2 A Review of Hierarchical Problem Networks 2.1 Representing Hierarchical Problem Networks 2.2 Hierarchical Problem Decomposition 3 Learning HPNs from Sample Solutions 3.1 Inputs to HPN Learning 3.2 Identifying HPN Structure 3.3 Inferring State Conditions 3.4 Identifying Goal Conditions 3.5 Implementation Details 4 Empirical Evaluation 5 Related Research 6 Concluding Remarks References Combining Word Embeddings-Based Similarity Measures for Transfer Learning Across Relational Domains 1 Introduction 2 Background 2.1 Functional Gradient Boosting of Relational Dependency Networks 2.2 Transfer Learning 2.3 Word Embeddings 2.4 Borda Count 3 Related Work 4 TransBoostler+ 4.1 Transferring and Revising the Structure 4.2 Combining Similarity Measures 5 Experimental Results 6 Conclusions References Learning Assumption-Based Argumentation Frameworks 1 Introduction 2 Background: Assumption-Based Argumentation (ABA) 3 The ABA Learning Problem 4 Transformation Rules for ABA Frameworks 5 Learning by Rebuttal and Undercutting Attacks 6 A Learning Strategy 7 Learning Circular Debates 8 Conclusions References Diagnosis of Event Sequences with LFIT 1 Introduction 2 Dynamical Multi-valued Logic Program 3 Diagnosis of Labelled Event Sequences 3.1 Modeling Labelled Event Sequences 3.2 Encoding Elementary LTL Operators 3.3 Encoding Complex LTL Properties 3.4 Discussion 4 Conclusion References Efficient Abductive Learning of Microbial Interactions Using Meta Inverse Entailment 1 Introduction 2 Background and Related Work 3 Abduction via Meta Inverse Entailment 4 Abduction of Microbial Interaction Using MIE 5 Empirical Evaluation 5.1 Materials and Methods 6 Results and Discussions 7 Conclusion References Functional Lifted Bayesian Networks: Statistical Relational Learning and Reasoning with Relative Frequencies 1 Introduction 1.1 Related Work 1.2 Queries Relating to Degrees of Belief vs Relative Frequency 1.3 Transfer Learning and Extrapolation 2 Defining Functional Lifted Bayesian Networks 3 Discussion and Applications 3.1 Expressivity of Functional Lifted Bayesian Networks 3.2 Learning Functional Lifted Bayesian Networks 3.3 Asymptotic Analysis of the Extrapolation Behaviour 3.4 Proof of Theorem 1 3.5 Examples 3.6 Transfer Learning Across Domain Sizes 4 Conclusion References Author Index