Parallel Constraint-Driven Inductive Logic Programming

09/15/2021
by   Andrew Cropper, et al.
0

Multi-core machines are ubiquitous. However, most inductive logic programming (ILP) approaches use only a single core, which severely limits their scalability. To address this limitation, we introduce parallel techniques based on constraint-driven ILP where the goal is to accumulate constraints to restrict the hypothesis space. Our experiments on two domains (program synthesis and inductive general game playing) show that (i) parallelisation can substantially reduce learning times, and (ii) worker communication (i.e. sharing constraints) is important for good performance.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

02/20/2022

Learning logic programs by discovering where not to search

The goal of inductive logic programming (ILP) is to search for a hypothe...
09/16/2021

Learning logic programs through divide, constrain, and conquer

We introduce an inductive logic programming approach that combines class...
10/12/2015

The Inductive Constraint Programming Loop

Constraint programming is used for a variety of real-world optimisation ...
11/22/2021

Parallel Logic Programming: A Sequel

Multi-core and highly-connected architectures have become ubiquitous, an...
04/29/2021

Predicate Invention by Learning From Failures

Discovering novel high-level concepts is one of the most important steps...
04/21/2020

Knowledge Refactoring for Program Induction

Humans constantly restructure knowledge to use it more efficiently. Our ...
04/21/2020

Learning large logic programs by going beyond entailment

A major challenge in inductive logic programming (ILP) is learning large...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.