Papers Inductive logic programming
“Inductive logic programming” 태그가 달린 논문 181편 · 필터 해제
Learning logic programs by discovering higher-order abstractions
We introduce the higher-order refactoring problem, where the goal is to compress a logic program by discovering higher-order abstractions, such as map, filter, and fold. We implement our approach in Stevie, which formula…
Inductive logic programmingProgram SynthesisVisual ReasoningLearning Logic Specifications for Soft Policy Guidance in POMCP
Partially Observable Monte Carlo Planning (POMCP) is an efficient solver for Partially Observable Markov Decision Processes (POMDPs). It allows scaling to large state spaces by computing an approximation of the optimal p…
Inductive logic programmingGeneralisation Through Negation and Predicate Invention
The ability to generalise from a small number of examples is a fundamental challenge in machine learning. To tackle this challenge, we introduce an inductive logic programming (ILP) approach that combines negation and pr…
Inductive logic programmingNegationA Divide-Align-Conquer Strategy for Program Synthesis
A major bottleneck in search-based program synthesis is the exponentially growing search space which makes learning large programs intractable. Humans mitigate this problem by leveraging the compositional nature of the r…
ARCInductive logic programmingProgram SynthesisVisual ReasoningA Model for Intelligible Interaction Between Agents That Predict and Explain
Machine Learning (ML) has emerged as a powerful form of data modelling with widespread applicability beyond its roots in the design of autonomous agents. However, relatively little attention has been paid to the interact…
Inductive logic programmingRelational program synthesis with numerical reasoning
Program synthesis approaches struggle to learn programs with numerical values. An especially difficult problem is learning continuous values over multiple examples, such as intervals. To overcome this limitation, we intr…
Inductive logic programmingProgram SynthesisRelational ReasoningConstraint-driven multi-task learning
Inductive logic programming is a form of machine learning based on mathematical logic that generates logic programs from given examples and background knowledge. In this project, we extend the Popper ILP system to make u…
Inductive logic programmingMulti-Task LearningDifferentiable Inductive Logic Programming in High-Dimensional Space
Synthesizing large logic programs through symbolic Inductive Logic Programming (ILP) typically requires intermediate definitions. However, cluttering the hypothesis space with intensional predicates typically degrades pe…
Inductive logic programmingVocal Bursts Intensity PredictionLearning programs with magic values
A magic value in a program is a constant symbol that is essential for the execution of the program but has no clear explanation for its choice. Learning programs with magic values is difficult for existing program synthe…
Drug DesignInductive logic programmingProgram SynthesisA Model-Oriented Approach for Lifting Symmetries in Answer Set Programming
When solving combinatorial problems, pruning symmetric solution candidates from the search space is essential. Most of the existing approaches are instance-specific and focus on the automatic computation of Symmetry Brea…
Inductive logic programmingTemporal Inductive Logic Reasoning over Hypergraphs
Inductive logic reasoning is a fundamental task in graph analysis, which aims to generalize patterns from data. This task has been extensively studied for traditional graph representations, such as knowledge graphs (KGs)…
Inductive logic programmingKnowledge GraphsComposition of Relational Features with an Application to Explaining Black-Box Predictors
Relational machine learning programs like those developed in Inductive Logic Programming (ILP) offer several advantages: (1) The ability to model complex relationships amongst data instances; (2) The use of domain-specif…
Inductive logic programmingLearning logic programs by combining programs
The goal of inductive logic programming is to induce a logic program (a set of logical rules) that generalises training examples. Inducing programs with many rules and literals is a major challenge. To tackle this challe…
Inductive logic programmingProgram SynthesisExplanatory machine learning for sequential human teaching
The topic of comprehensibility of machine-learned theories has recently drawn increasing attention. Inductive Logic Programming (ILP) uses logic programming to derive logic theories from small data based on abduction and…
BIG-bench Machine LearningInductive logic programmingEfficient lifting of symmetry breaking constraints for complex combinatorial problems
Many industrial applications require finding solutions to challenging combinatorial problems. Efficient elimination of symmetric solution candidates is one of the key enablers for high-performance solving. However, exist…
Inductive logic programmingLearning First-Order Rules with Differentiable Logic Program Semantics
Learning first-order logic programs (LPs) from relational facts which yields intuitive insights into the data is a challenging topic in neuro-symbolic research. We introduce a novel differentiable inductive logic program…
Inductive logic programmingAn Interactive Explanatory AI System for Industrial Quality Control
Machine learning based image classification algorithms, such as deep neural network approaches, will be increasingly employed in critical settings such as quality control in industry, where transparency and comprehensibi…
BIG-bench Machine LearningDefect Detectionimage-classificationImage Classification+1Learning logic programs by discovering where not to search
The goal of inductive logic programming (ILP) is to search for a hypothesis that generalises training examples and background knowledge (BK). To improve performance, we introduce an approach that, before searching for a …
Inductive logic programmingProgram SynthesisA Critical Review of Inductive Logic Programming Techniques for Explainable AI
Despite recent advances in modern machine learning algorithms, the opaqueness of their underlying mechanisms continues to be an obstacle in adoption. To instill confidence and trust in artificial intelligence systems, Ex…
BIG-bench Machine LearningExplainable artificial intelligenceInductive logic programmingRelational ReasoningLearning Higher-Order Programs without Meta-Interpretive Learning
Learning complex programs through inductive logic programming (ILP) remains a formidable challenge. Existing higher-order enabled ILP systems show improved accuracy and learning performance, though remain hampered by the…
Inductive logic programming