Inductive logic programming
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Benchmarks
RuDaS
Most implemented
CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text
Inductive logic programming at 30: a new introduction
Learning Explanatory Rules from Noisy Data
Differentiable Inductive Logic Programming in High-Dimensional Space
Learning programs with magic values
Incorporating Symbolic Domain Knowledge into Graph Neural Networks
Papers
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 programming