Inductive logic programming

33 papers with code • 1 benchmarks • 2 datasets

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Use these libraries to find Inductive logic programming models and implementations
2 papers

Most implemented papers

CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text

facebookresearch/clutrr IJCNLP 2019

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

ai-systems/DILP-Core 13 Nov 2017

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

Neural Logic Machines

google/neural-logic-machines ICLR 2019

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

Inductive general game playing

andrewcropper/iggp 23 Jun 2019

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

Learning higher-order logic programs

metagol/metagol 25 Jul 2019

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

metagol/metagol 15 Nov 2019

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

tirtharajdash/VEGNN 23 Oct 2020

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.

Incremental Learning of Event Definitions with Inductive Logic Programming

nkatzz/iled 24 Feb 2014

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.

Probabilistic Inductive Logic Programming Based on Answer Set Programming

MatthiasNickles/diff-SAT 4 May 2014

We propose a new formal language for the expressive representation of probabilistic knowledge based on Answer Set Programming (ASP).

Online Learning of Event Definitions

nkatzz/OLED 30 Jul 2016

The Event Calculus is a temporal logic that has been used as a basis in event recognition applications, providing among others, direct connections to machine learning, via Inductive Logic Programming (ILP).