scholarly journals Inductive logic programming at 30

2021 ◽  
Author(s):  
Andrew Cropper ◽  
Sebastijan Dumančić ◽  
Richard Evans ◽  
Stephen H. Muggleton
Keyword(s):  
Machine Learning ◽  
Logic Programming ◽  
Inductive Logic Programming ◽  
Logic Program ◽  
Inductive Logic ◽  
Future Research ◽  
Predicate Invention ◽  
Meta Level ◽  
Recursive Programs ◽  
Training Examples

AbstractInductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge. As ILP turns 30, we review the last decade of research. We focus on (i) new meta-level search methods, (ii) techniques for learning recursive programs, (iii) new approaches for predicate invention, and (iv) the use of different technologies. We conclude by discussing current limitations of ILP and directions for future research.

scholarly journals Turning 30: New Ideas in Inductive Logic Programming

2020 ◽  
Author(s):  
Andrew Cropper ◽  
Sebastijan Dumančić ◽  
Stephen H. Muggleton
Keyword(s):  
Machine Learning ◽  
Logic Programming ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Background Knowledge ◽  
Training Data ◽  
Future Research ◽  
Logic Programs ◽  
Recursive Programs ◽  
New Ideas

Common criticisms of state-of-the-art machine learning include poor generalisation, a lack of interpretability, and a need for large amounts of training data. We survey recent work in inductive logic programming (ILP), a form of machine learning that induces logic programs from data, which has shown promise at addressing these limitations. We focus on new methods for learning recursive programs that generalise from few examples, a shift from using hand-crafted background knowledge to learning background knowledge, and the use of different technologies, notably answer set programming and neural networks. As ILP approaches 30, we also discuss directions for future research.

scholarly journals Inductive general game playing

2019 ◽  
Vol 109 (7) ◽  
pp. 1393-1434
Author(s):  
Andrew Cropper ◽  
Richard Evans ◽  
Mark Law
Keyword(s):  
Logic Programming ◽  
Inductive Logic Programming ◽  
Logic Program ◽  
Inductive Logic ◽  
Future Research ◽  
General Intelligence ◽  
Game Playing ◽  
General Game Playing ◽  
Wide Range ◽  
General Game

AbstractGeneral game playing (GGP) is a framework for evaluating an agent’s general intelligence across a wide range of tasks. In the GGP competition, an agent is given the rules of a game (described as a logic program) that it has never seen before. The task is for the agent to play the game, thus generating game traces. The winner of the GGP competition is the agent that gets the best total score over all the games. In this paper, we invert this task: a learner is given game traces and the task is to learn the rules that could produce the traces. This problem is central to inductive general game playing (IGGP). We introduce a technique that automatically generates IGGP tasks from GGP games. We introduce an IGGP dataset which contains traces from 50 diverse games, such as Sudoku, Sokoban, and Checkers. We claim that IGGP is difficult for existing inductive logic programming (ILP) approaches. To support this claim, we evaluate existing ILP systems on our dataset. Our empirical results show that most of the games cannot be correctly learned by existing systems. The best performing system solves only 40% of the tasks perfectly. Our results suggest that IGGP poses many challenges to existing approaches. Furthermore, because we can automatically generate IGGP tasks from GGP games, our dataset will continue to grow with the GGP competition, as new games are added every year. We therefore think that the IGGP problem and dataset will be valuable for motivating and evaluating future research.

Discovering and organizing noun-verb collocations in specialized corpora using inductive logic programming

2006 ◽  
Vol 11 (2) ◽  
pp. 209-243 ◽  
Author(s):  
Vincent Claveau ◽  
Marie-Claude L'Homme
Keyword(s):  
Machine Learning ◽  
Logic Programming ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Success Rates ◽  
Machine Learning Technique ◽  
Acquisition Process ◽  
Semantic Relationships ◽  
Learning Technique ◽  
French Corpus

This article presents a method for discovering and organizing noun-verb (N-V) combinations found in a French corpus on computing. Our aim is to find N-V combinations in which verbs convey a “realization meaning” as defined in the framework of lexical functions (Mel’čuk 1996, 1998). Our approach, chiefly corpus-based, uses a machine learning technique, namely Inductive Logic Programming (ILP). The whole acquisition process is divided into three steps: (1) isolating contexts in which specific N-V pairs occur; (2) inferring linguistically-motivated rules that reflect the behaviour of realization N-V pairs; (3) projecting these rules on corpora to find other valid N-V pairs. This technique is evaluated in terms of the relevance of the rules inferred and in terms of the quality (recall and precision) of the results. Results obtained show that our approach is able to find these very specific semantic relationships (the realization N-V pairs) with very good success rates.

Inductive logic programming applied for knowledge representation in computer music/ Programação lógica indutiva aplicada para representação do conhecimento em música computacional

2021 ◽  
Vol 5 (4) ◽  
pp. 1840-1857
Author(s):  
Clenio B. Gonçalves Junior ◽  
Murillo Rodrigo Petrucelli Homem
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Knowledge Representation ◽  
Logic Programming ◽  
Inductive Logic Programming ◽  
Computer Music ◽  
Inductive Logic ◽  
Essential Element ◽  
Research Field ◽  
Musical Knowledge

 In Computer Music, the knowledge representation process is an essential element for the development of systems. Methods have been applied to provide the computer with the ability to generate conclusions based on previously established experience and definitions. In this sense, Inductive Logic Programming presents itself as a research field that incorporates concepts of Logic Programming and Machine Learning, its declarative character allows musical knowledge to be presented to non-specialist users in a naturally understandable way. The present work performs a systematic review based on approaches that use Inductive Logic Programming in the representation of musical knowledge. Questions that these studies seek to address were raised, as well as identifying characteristic aspects related to their application.

scholarly journals Learning Higher-Order Programs through Predicate Invention

2020 ◽  
Vol 34 (09) ◽  
pp. 13655-13658
Author(s):  
Andrew Cropper ◽  
Rolf Morel ◽  
Stephen H. Muggleton
Keyword(s):  
Logic Programming ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Higher Order ◽  
Predicate Invention ◽  
First Order

A key feature of inductive logic programming (ILP) is its ability to learn first-order programs, which are intrinsically more expressive than propositional programs. In this paper, we introduce ILP techniques to learn higher-order programs. We implement our idea in Metagolho, an ILP system which can learn higher-order programs with higher-order predicate invention. Our experiments show that, compared to first-order programs, learning higher-order programs can significantly improve predictive accuracies and reduce learning times.

Inductive Logic Programming and Embodied Agents

2011 ◽  
pp. 116-131
Author(s):  
Andrea Kulakov ◽  
Joona Laukkanen ◽  
Blerim Mustafa ◽  
Georgi Stojanov
Keyword(s):  
Machine Learning ◽  
Logic Programming ◽  
Inductive Logic Programming ◽  
Autonomous Agents ◽  
Inductive Logic ◽  
Large Datasets ◽  
Embodied Agents ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Intelligent Robotics

Open-ended learning is regarded as the ultimate milestone, especially in intelligent robotics. Preferably it should be unsupervised and it is by its nature inductive. In this article we want to give an overview of attempts to use Inductive Logic Programming (ILP) as a machine learning technique in the context of embodied autonomous agents. Relatively few such attempts exist altogether and the main goal in reviewing several of them was to find a thorough understanding of the difficulties that the application of ILP has in general and especially in this area. The second goal was to review any possible directions for overcoming these obstacles standing on the way of more widespread use of ILP in this context of embodied autonomous agents. Whilst the most serious problems, the mismatch between ILP and the large datasets encountered with embodied autonomous agents seem difficult to overcome we also found interesting research actively pursuing to alleviate these problems.

Sign in / Sign up

Export Citation Format

Share Document