Titlebook: Inductive Logic Programming; 30th International C Nikos Katzouris,Alexander Artikis Conference proceedings 2022 Springer Nature Switzerland

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A First Step Towards Even More Sparse Encodings of Probability Distributions,n exponential number of values. Hence, we propose a method for extracting first-order formulas from probability distributions that require significantly less values by reducing the number of values in a distribution and then extracting, for each value, a logical formula to be further minimized. This

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,Learning Logic Programs Using Neural Networks by Exploiting Symbolic Invariance,IT algorithms have mainly been implemented in the symbolic method, but they are not robust to noisy or missing data. Recently, research works combining logical operations with neural networks are receiving a lot of attention, with most works taking an extraction based approach where a single neural

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Human-Like Rule Learning from Images Using One-Shot Hypothesis Derivation,ple. Humans achieve this ability using background knowledge. Rule-based machine learning approaches such as Inductive Logic Programming (ILP) provide a framework for incorporating domain specific background knowledge. These approaches have the potential for human-like learning from small data or eve

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Generative Clausal Networks: Relational Decision Trees as Probabilistic Circuits,t hand. Recently, a clear connection between predictive modelling such as decision trees and probabilistic circuits, a form of deep probabilistic model, has been established although it is limited to propositional data. We introduce the first connection between relational rule models and probabilist

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