Papers Inductive logic programming
“Inductive logic programming” 태그가 달린 논문 181편 · 필터 해제
Learning Logic Programs From Noisy Failures
Inductive Logic Programming (ILP) is a form of machine learning (ML) which in contrast to many other state of the art ML methods typically produces highly interpretable and reusable models. However, many ILP systems lack…
Inductive logic programmingNeuro-Symbolic Hierarchical Rule Induction
We propose an efficient interpretable neuro-symbolic model to solve Inductive Logic Programming (ILP) problems. In this model, which is built from a set of meta-rules organised in a hierarchical structure, first-order ru…
Inductive logic programmingreinforcement-learningReinforcement Learning (RL)Lifting Symmetry Breaking Constraints with Inductive Logic Programming
Efficient omission of symmetric solution candidates is essential for combinatorial problem-solving. Most of the existing approaches are instance-specific and focus on the automatic computation of Symmetry Breaking Constr…
Inductive logic programmingPreprocessing in Inductive Logic Programming
Inductive logic programming is a type of machine learning in which logic programs are learned from examples. This learning typically occurs relative to some background knowledge provided as a logic program. This disserta…
Inductive logic programmingLogical Boltzmann Machines
The idea of representing symbolic knowledge in connectionist systems has been a long-standing endeavour which has attracted much attention recently with the objective of combining machine learning and scalable sound reas…
Inductive logic programmingNeuro-Symbolic Inductive Logic Programming with Logical Neural Networks
Recent work on neuro-symbolic inductive logic programming has led to promising approaches that can learn explanatory rules from noisy, real-world data. While some proposals approximate logical operators with differentiab…
BenchmarkingInductive logic programmingOntology-Based Skill Description Learning for Flexible Production Systems
The increasing importance of resource-efficient production entails that manufacturing companies have to create a more dynamic production environment, with flexible manufacturing machines and processes. To fully utilize t…
Inductive logic programmingA Hybrid Neuro-Symbolic approach for Text-Based Games using Inductive Logic Programming
Text-based games (TBGs) have emerged as an important test-bed, requiring reinforcement learning (RL) agents to combine natural language understanding with reasoning. A key challenge for agents solving this task is to gen…
Inductive logic programmingNatural Language UnderstandingReinforcement Learning (RL)text-based gamesUsing Program Synthesis and Inductive Logic Programming to solve Bongard Problems
The ability to recognise and make analogies is often used as a measure or test of human intelligence. The ability to solve Bongard problems is an example of such a test. It has also been postulated that the ability to ra…
Inductive logic programmingProgram SynthesisLearning Rules with Stratified Negation in Differentiable ILP.
Differentiable methods to learn rules (logic programs) have the potential to integrate the interpretability, transferability and low data requirements of inductive logic programming with the noise tolerance of non-symbol…
Inductive logic programmingNegationOn the Complexity of Inductively Learning Guarded Rules
We investigate the computational complexity of mining guarded clauses from clausal datasets through the framework of inductive logic programming (ILP). We show that learning guarded clauses is NP-complete and thus one st…
Inductive logic programmingExplanation as a process: user-centric construction of multi-level and multi-modal explanations
In the last years, XAI research has mainly been concerned with developing new technical approaches to explain deep learning models. Just recent research has started to acknowledge the need to tailor explanations to diffe…
Explainable Artificial Intelligence (XAI)Inductive logic programmingInterpretable Machine LearningA Clustering and Demotion Based Algorithm for Inductive Learning of Default Theories
We present a clustering- and demotion-based algorithm called Kmeans-FOLD to induce nonmonotonic logic programs from positive and negative examples. Our algorithm improves upon-and is inspired by-the FOLD algorithm. The F…
ClusteringInductive LearningInductive logic programmingLearning logic programs through divide, constrain, and conquer
We introduce an inductive logic programming approach that combines classical divide-and-conquer search with modern constraint-driven search. Our anytime approach can learn optimal, recursive, and large programs and suppo…
Inductive logic programmingProgram SynthesisCombining Rules and Embeddings via Neuro-Symbolic AI for Knowledge Base Completion
Recent interest in Knowledge Base Completion (KBC) has led to a plethora of approaches based on reinforcement learning, inductive logic programming and graph embeddings. In particular, rule-based KBC has led to interpret…
Inductive logic programmingKnowledge Base CompletionRelationParallel Constraint-Driven Inductive Logic Programming
Multi-core machines are ubiquitous. However, most inductive logic programming (ILP) approaches use only a single core, which severely limits their scalability. To address this limitation, we introduce parallel techniques…
Inductive logic programmingProgram SynthesisFF-NSL: Feed-Forward Neural-Symbolic Learner
Logic-based machine learning aims to learn general, interpretable knowledge in a data-efficient manner. However, labelled data must be specified in a structured logical form. To address this limitation, we propose a neur…
Inductive logic programmingInterpretable Model-based Hierarchical Reinforcement Learning using Inductive Logic Programming
Recently deep reinforcement learning has achieved tremendous success in wide ranges of applications. However, it notoriously lacks data-efficiency and interpretability. Data-efficiency is important as interacting with th…
Deep Reinforcement LearningHierarchical Reinforcement LearningInductive logic programmingreinforcement-learning+2Generating Contrastive Explanations for Inductive Logic Programming Based on a Near Miss Approach
In recent research, human-understandable explanations of machine learning models have received a lot of attention. Often explanations are given in form of model simplifications or visualizations. However, as shown in cog…
Explanation GenerationInductive logic programmingManagementInclusion of Domain-Knowledge into GNNs using Mode-Directed Inverse Entailment
We present a general technique for constructing Graph Neural Networks (GNNs) capable of using multi-relational domain knowledge. The technique is based on mode-directed inverse entailment (MDIE) developed in Inductive Lo…
FormInductive logic programming