Inductive Logic Programming

Table of Contents

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   Book Overview
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   Chapter 1 Inference and Learning in Planning
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   Chapter 2 Beyond Reward: The Problem of Knowledge and Data
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   Chapter 3 Exploiting Constraints
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   Chapter 4 Online Bayesian Inference for the Parameters of PRISM Programs
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   Chapter 5 Learning Compact Markov Logic Networks with Decision Trees
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   Chapter 6 Relational Networks of Conditional Preferences
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   Chapter 7 k -Optimal: A Novel Approximate Inference Algorithm for ProbLog
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   Chapter 8 Learning Directed Relational Models with Recursive Dependencies
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    Chapter 9 Integrating Model Checking and Inductive Logic Programming
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    Chapter 10 Learning the Structure of Probabilistic Logic Programs
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    Chapter 11 Subgroup Discovery Using Bump Hunting on Multi-relational Histograms
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    Chapter 12 Inductive Logic Programming in Answer Set Programming
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    Chapter 13 Graph-Based Relational Learning with a Polynomial Time Projection Algorithm
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    Chapter 14 Interleaved Inductive-Abductive Reasoning for Learning Complex Event Models
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    Chapter 15 Predictive Sequence Miner in ILP Learning
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    Chapter 16 Conceptual Clustering of Multi-Relational Data
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    Chapter 17 Expressive Power of Safe First-Order Logical Decision Trees
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    Chapter 18 DNF Hypotheses in Explanatory Induction
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    Chapter 19 Variational Bayes Inference for Logic-Based Probabilistic Models on BDDs
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    Chapter 20 Relational Learning for Spatial Relation Extraction from Natural Language
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    Chapter 21 Does Multi-Clause Learning Help in Real-World Applications?
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    Chapter 22 MC-TopLog: Complete Multi-clause Learning Guided by a Top Theory
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    Chapter 23 Integrating Relational Reinforcement Learning with Reasoning about Actions and Change
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    Chapter 24 Efficient Operations in Feature Terms Using Constraint Programming
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    Chapter 25 Learning Theories Using Estimation Distribution Algorithms and (Reduced) Bottom Clauses
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    Chapter 26 Active Learning of Relational Action Models
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    Chapter 27 Knowledge-Guided Identification of Petri Net Models of Large Biological Systems
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    Chapter 28 Machine Learning a Probabilistic Network of Ecological Interactions
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    Chapter 29 Kernel-Based Logical and Relational Learning with kLog for Hedge Cue Detection
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    Chapter 30 Projection-Based PILP: Computational Learning Theory with Empirical Results
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    Chapter 31 Comparison of Upward and Downward Generalizations in CF-Induction
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    Chapter 32 Polynomial Time Inductive Inference of Cograph Pattern Languages from Positive Data