# Inductive logic programming

43 papers with code • 1 benchmarks • 2 datasets

## Libraries

Use these libraries to find Inductive logic programming models and implementations## Most implemented papers

# CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text

The recent success of natural language understanding (NLU) systems has been troubled by results highlighting the failure of these models to generalize in a systematic and robust way.

# Learning Explanatory Rules from Noisy Data

Artificial Neural Networks are powerful function approximators capable of modelling solutions to a wide variety of problems, both supervised and unsupervised.

# Inductive logic programming at 30: a new introduction

Inductive logic programming (ILP) is a form of machine learning.

# Neural Logic Machines

We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both inductive learning and logic reasoning.

# Inductive general game playing

This problem is central to inductive general game playing (IGGP).

# Learning higher-order logic programs

Our theoretical results show that learning higher-order programs, rather than first-order programs, can reduce the textual complexity required to express programs which in turn reduces the size of the hypothesis space and sample complexity.

# Forgetting to learn logic programs

To improve learning performance, we explore the idea of forgetting, where a learner can additionally remove programs from its BK.

# Incorporating Symbolic Domain Knowledge into Graph Neural Networks

These kinds of problems have been addressed effectively in the past by Inductive Logic Programming (ILP), by virtue of 2 important characteristics: (a) The use of a representation language that easily captures the relation encoded in graph-structured data, and (b) The inclusion of prior information encoded as domain-specific relations, that can alleviate problems of data scarcity, and construct new relations.

# Learning 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.

# Incremental Learning of Event Definitions with Inductive Logic Programming

Ideally, systems that learn from temporal data should be able to operate in an incremental mode, that is, revise prior constructed knowledge in the face of new evidence.