Papers Inductive logic programming
“Inductive logic programming” 태그가 달린 논문 181편 · 필터 해제
Symbolic Snapshot Ensembles
Inductive logic programming (ILP) is a form of logical machine learning. Most ILP algorithms learn a single hypothesis from a single training run. Ensemble methods train an ILP algorithm multiple times to learn multiple …
Inductive logic programmingVisual ReasoningSymbolic Neural Generation with Applications to Lead Discovery in Drug Design
We investigate a relatively under-explored class of hybrid neurosymbolic models that integrate symbolic learning with neural reasoning to construct data generators meeting formal correctness criteria. In Symbolic Neural …
Inductive logic programmingAbductive Logical Rule Induction by Bridging Inductive Logic Programming and Multimodal Large Language Models
We propose ILP-CoT, a method that bridges Inductive Logic Programming (ILP) and Multimodal Large Language Models (MLLMs) for abductive logical rule induction. The task involves both discovering logical facts and inducing…
Inductive logic programmingLearning Logical Rules using Minimum Message Length
Unifying probabilistic and logical learning is a key challenge in AI. We introduce a Bayesian inductive logic programming approach that learns minimum message length hypotheses from noisy data. Our approach balances hypo…
Inductive logic programmingSymmetry breaking for inductive logic programming
The goal of inductive logic programming is to search for a hypothesis that generalises training data and background knowledge. The challenge is searching vast hypothesis spaces, which is exacerbated because many logicall…
Inductive logic programmingVisual ReasoningGLIDR: Graph-Like Inductive Logic Programming with Differentiable Reasoning
Differentiable inductive logic programming (ILP) techniques have proven effective at finding approximate rule-based solutions to link prediction and node classification problems on knowledge graphs; however, the common a…
Inductive logic programmingKnowledge Graph CompletionNode ClassificationKnowledge GraphsSelf-Supervised Inductive Logic Programming
Inductive Logic Programming (ILP) approaches like Meta \-/ Interpretive Learning (MIL) can learn, from few examples, recursive logic programs with invented predicates that generalise well to unseen instances. This abilit…
Inductive logic programmingModelling Program Spaces in Program Synthesis with Constraints
A core challenge in program synthesis is taming the large space of possible programs. Since program synthesis is essentially a combinatorial search, the community has sought to leverage powerful combinatorial constraint …
Inductive logic programmingProgram SynthesisLearning Symbolic Persistent Macro-Actions for POMDP Solving Over Time
This paper proposes an integration of temporal logical reasoning and Partially Observable Markov Decision Processes (POMDPs) to achieve interpretable decision-making under uncertainty with macro-actions. Our method lever…
Computational EfficiencyDecision MakingDecision Making Under UncertaintyInductive logic programming+1Neuro-symbolic Weak Supervision: Theory and Semantics
Weak supervision allows machine learning models to learn from limited or noisy labels, but it introduces challenges in interpretability and reliability - particularly in multi-instance partial label learning (MI-PLL), wh…
Inductive logic programmingPartial Label LearningAn Empirical Comparison of Cost Functions in Inductive Logic Programming
Recent inductive logic programming (ILP) approaches learn optimal hypotheses. An optimal hypothesis minimises a given cost function on the training data. There are many cost functions, such as minimising training error, …
Inductive logic programmingProgram SynthesisNeuro-Symbolic Contrastive Learning for Cross-domain Inference
Pre-trained language models (PLMs) have made significant advances in natural language inference (NLI) tasks, however their sensitivity to textual perturbations and dependence on large datasets indicate an over-reliance o…
Contrastive LearningInductive logic programmingNatural Language InferenceProceedings 40th International Conference on Logic Programming
Since the first conference In Marseille in 1982, the International Conference on Logic Programming (ICLP) has been the premier international event for presenting research in logic programming. These proceedings include t…
Inductive logic programmingProbabilistic ProgrammingPrompt EngineeringInductive Learning of Robot Task Knowledge from Raw Data and Online Expert Feedback
The increasing level of autonomy of robots poses challenges of trust and social acceptance, especially in human-robot interaction scenarios. This requires an interpretable implementation of robotic cognitive capabilities…
Inductive LearningInductive logic programmingOnline inductive learning from answer sets for efficient reinforcement learning exploration
This paper presents a novel approach combining inductive logic programming with reinforcement learning to improve training performance and explainability. We exploit inductive learning of answer set programs from noisy e…
Inductive LearningInductive logic programmingQ-Learningreinforcement-learning+1HT-HEDL: High-Throughput Hypothesis Evaluation in Description Logic
We present High-Throughput Hypothesis Evaluation in Description Logic (HT-HEDL). HT-HEDL is a high-performance hypothesis evaluation engine that accelerates hypothesis evaluation computations for inductive logic programm…
CPUGPUInductive logic programmingLearning Rules Explaining Interactive Theorem Proving Tactic Prediction
Formally verifying the correctness of mathematical proofs is more accessible than ever, however, the learning curve remains steep for many of the state-of-the-art interactive theorem provers (ITP). Deriving the most appr…
Automated Theorem ProvingInductive logic programmingMathematical ProofsPredictionDifferentiable Inductive Logic Programming for Fraud Detection
Current trends in Machine Learning prefer explainability even when it comes at the cost of performance. Therefore, explainable AI methods are particularly important in the field of Fraud Detection. This work investigates…
Fraud DetectionInductive logic programmingLLM-Augmented Symbolic Reinforcement Learning with Landmark-Based Task Decomposition
One of the fundamental challenges in reinforcement learning (RL) is to take a complex task and be able to decompose it to subtasks that are simpler for the RL agent to learn. In this paper, we report on our work that wou…
Common Sense ReasoningInductive logic programmingLanguage ModelingLanguage Modelling+4Learning Robust Reward Machines from Noisy Labels
This paper presents PROB-IRM, an approach that learns robust reward machines (RMs) for reinforcement learning (RL) agents from noisy execution traces. The key aspect of RM-driven RL is the exploitation of a finite-state …
Inductive logic programmingReinforcement Learning (RL)