scholarly journals Learning relations and logic programs

1994 ◽  
Vol 9 (1) ◽  
pp. 73-77 ◽  
Author(s):  
F. Bergadano ◽  
D. Gunetti
Keyword(s):  
Machine Learning ◽  
Logic Programming ◽  
Inductive Logic ◽  
Computational Learning Theory ◽  
Future Generation ◽  
Order Theory ◽  
Research Area ◽  
Theory Revision ◽  
First Order Theory ◽  
Generation Computer

Inductive Logic Programming (ILP) is an emerging research area at the intersection of machine learning, logic programming and software engineering. The first workshop on this topic was held in 1991 in Portugal (Muggleton, 1991). Subsequently, there was a workshop tied to the Future Generation Computer System Conference in Japan in 1992, and a third one in Bled, Slovenia, in April 1993 (Muggleton, 1993). Ideas related to ILP are also appearing in major AI and machine learning conferences and journals. Although European-based and mainly sponsored by ESPRIT, ILP aims at becoming equally represented elsewhere; for example, among researchers in America who are investigating relational learning and first order theory revision (see, for example, the papers in Birnbaum and Collins, 1991) and within the computational learning theory community. This year's IJCAI workshop on ILP is a first step in this direction, and includes recent work with a broader range of perspectives and techniques.

AN IMPROVEMENT TO TOP-DOWN CLAUSE SPECIALIZATION

1998 ◽  
Vol 07 (01) ◽  
pp. 71-102
Author(s):  
PO-CHI CHEN ◽  
SUH-YIN LEE
Keyword(s):  
Machine Learning ◽  
Logic Programming ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Hill Climbing ◽  
Theory Revision ◽  
Time Increase ◽  
Top Down ◽  
Relational Domains ◽  
Remarkable Progress

One remarkable progress of recent research in machine learning is inductive logic programming (ILP). In most ILP system, clause specialization is one of the most important tasks. Usually, the clause specialization is performed by adding a literal at a time using hill-climbing heuristics. However, the single-literal addition can be caught by local pits when more than one literal needs to be added at a time increase the accuracy. Several techniques have been proposed for this problem but are restricted to relational domains. In this paper, we propose a technique called structure subtraction to construct a set of candidates for adding literals, single-literal or multiple-literals. This technique can be employed in any ILP system using top-down specilization and is not restricted to relational domains. A theory revision system is described to illustrate the use of structural subtraction.

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.

Probabilistic First-Order Theory Revision from Examples

2005 ◽  
pp. 295-311 ◽  
Author(s):  
Aline Paes ◽  
Kate Revoredo ◽  
Gerson Zaverucha ◽  
Vitor Santos Costa
Keyword(s):  
Order Theory ◽  
Theory Revision ◽  
First Order ◽  
First Order Theory ◽  
Theory Revision From Examples

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.

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.

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