A boxology of design patterns for hybrid learning and reasoning systems

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Abstract

We propose a set of design patterns to describe a large variety of systems that combine statistical techniques from machine learning with symbolic techniques from knowledge representation. As in other areas of computer science (knowledge engineering, software engineering, process mining), such design patterns help to systematize the literature, clarify which combinations of techniques serve which purposes, and encourage re-use of software components. We have validated our compositional design patterns against a large body of recent literature.

Original languageEnglish
Title of host publicationBNAIC/BENELEARN 2019 Proceedings of the Reference AI & ML Conference for Belgium, Netherlands & Luxemburg
Subtitle of host publicationProceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019) Brussels, Belgium, November 6-8, 2019
EditorsKatrien Beuls, Bart Bogaerts, Gianluca Bontempi, Pierre Geurts, Nick Harley, Bertrand Lebichot, Tom Lenaerts, Gilles Louppe, Paul Van Eecke
PublisherCEUR-WS
Pages1-2
Number of pages2
Publication statusPublished - 8 Nov 2019
Event31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning, BNAIC/BENELEARN 2019 - Brussels, Belgium
Duration: 6 Nov 20198 Nov 2019

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR Workshop Proceedings
Volume2491
ISSN (Print)1613-0073

Conference

Conference31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning, BNAIC/BENELEARN 2019
CountryBelgium
CityBrussels
Period6/11/198/11/19

Bibliographical note

Full version in: Journal of Web Engineering, vol: 18, combined Issue: 1, 2 & 3 (art.3 p. 97-124), published in: January 2019.

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