Papers Inductive logic programming
“Inductive logic programming” 태그가 달린 논문 181편 · 필터 해제
An Unofficial FastLAS Tutorial: A Programmer's Guide
FastLAS is a scalable system for Inductive Logic Programming (ILP): you give it some background knowledge, a language bias, and a set of examples, and it searches for a set of logic program rules (a hypothesis) that expl…
Inductive logic programmingReason Popper-ly: Patching In-Context Reasoning with Inductive Logic Programming
Chain-of-thought (CoT) prompting enables large language models (LLMs) to tackle multi-step reasoning tasks, yet the generated intermediate steps are not guaranteed to be logically sound. We present Reason Popper-ly, a ne…
Inductive logic programmingExplaining Weather Bulletins via ILP
Inductive Logic Programming (ILP) originated within the Logic Programming community in the Nineties as a framework for combining symbolic learning with declarative knowledge representation. Nowadays, mature ILP framework…
Inductive logic programmingADVENT: LLM-Driven Automatic Predicate Invention for ILP
Predicate invention (PI), the creation of new predicates to extend the hypothesis space, remains a critical bottleneck in Inductive Logic Programming (ILP). Existing methods rely on domain expertise and produce semantica…
Inductive logic programmingAutoSpec: Safety Rule Evolution for LLM Agents via Inductive Logic Programming
Large language model (LLM) agents increasingly automate complex tasks by integrating language models with external tools and environments. However, their autonomy poses significant safety risks: agents may execute destru…
Inductive logic programmingLearning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming
Learning from Demonstration~(LfD) should capture not only how a task is executed, but also its high-level task structure that explains the demonstrated behavior. As robots become more autonomous, such task representation…
Inductive logic programmingFrom Circuit Evidence to Mechanistic Theory: An Inductive Logic Approach
Mechanistic interpretability produces circuit-level causal analyses of neural network behaviour, but discovered circuits often remain isolated experimental artefacts: there is no shared formal representation for what cir…
Inductive logic programmingA Foundation Model for Zero-Shot Logical Rule Induction
Inductive Logic Programming (ILP) learns interpretable logical rules from data. Existing methods are transductive: their learned parameters are bound to specific predicates and require retraining for each new task. We in…
Inductive logic programmingANDRE: An Attention-based Neuro-symbolic Differentiable Rule Extractor for Inductive Logic Programming
Inductive Logic Programming (ILP) aims to learn interpretable first-order rules from data, but existing symbolic and neuro-symbolic approaches struggle to scale to noisy and probabilistic settings. Classical ILP relies o…
Inductive logic programmingWeakly Supervised Concept Learning for Object-centric Visual Reasoning
Neurosymbolic systems promise to combine deep neural network's (DNN) processing of raw sensor inputs with few-shot performance of symbolic artificial intelligence. Two-stage approaches explicitly decouple DNN based perce…
Inductive logic programmingDomain GeneralizationVisual ReasoningAGEL-Comp: A Neuro-Symbolic Framework for Compositional Generalization in Interactive Agents
Large Language Model (LLM)-based agents exhibit systemic failures in compositional generalization, limiting their robustness in interactive environments. This work introduces AGEL-Comp, a neuro-symbolic AI agent architec…
Inductive logic programmingExplaining Neural Networks in Preference Learning: a Post-hoc Inductive Logic Programming Approach
In this paper, we propose using Learning from Answer Sets to approximate black-box models, such as Neural Networks (NN), in the specific case of learning user preferences. We specifically explore the use of ILASP (Induct…
Inductive logic programmingWorld2Rules: A Neuro-Symbolic Framework for Learning World-Governing Safety Rules for Aviation
Many real-world safety-critical systems are governed by explicit rules that define unsafe world configurations and constrain agent interactions. In practice, these rules are complex and context-dependent, making manual s…
Inductive logic programmingDifferentiable Rule Induction from Raw Sequence Inputs
Rule learning-based models are widely used in highly interpretable scenarios due to their transparent structures. Inductive logic programming (ILP), a form of machine learning, induces rules from facts while maintaining …
Inductive logic programmingObject-Oriented Transition Modeling with Inductive Logic Programming
Building models of the world from observation, i.e., induction, is one of the major challenges in machine learning. In order to be useful, models need to maintain accuracy when used in novel situations, i.e., generalize.…
Inductive logic programmingXAI-LAW: A Logic Programming Tool for Modeling, Explaining, and Learning Legal Decisions
We propose an approach to model articles of the Italian Criminal Code (ICC), using Answer Set Programming (ASP), and to semi-automatically learn legal rules from examples based on prior judicial decisions. The developed …
Inductive logic programmingSatisfiability Modulo Theory Meets Inductive Logic Programming
Inductive Logic Programming (ILP) provides interpretable rule learning in relational domains, yet remains limited in its ability to induce and reason with numerical constraints. Classical ILP systems operate over discret…
Inductive logic programmingNeuro-Logic Lifelong Learning
Solving Inductive Logic Programming (ILP) problems with neural networks is a key challenge in Neural-Symbolic Ar- tificial Intelligence (AI). While most research has focused on designing novel network architectures for i…
Inductive logic programmingAdaptive GR(1) Specification Repair for Liveness-Preserving Shielding in Reinforcement Learning
Shielding is widely used to enforce safety in reinforcement learning (RL), ensuring that an agent's actions remain compliant with formal specifications. Classical shielding approaches, however, are often static, in the s…
Inductive logic programmingReinforcement LearningPredicate Renaming via Large Language Models
In this paper, we address the problem of giving names to predicates in logic rules using Large Language Models (LLMs). In the context of Inductive Logic Programming, various rule generation methods produce rules containi…
Inductive logic programming