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Modular design patterns for hybrid learning and reasoning systems: a taxonomy, patterns and use cases

van Bekkum, Michael, de Boer, Maaike, van Harmelen, Frank, Meyer-Vitali, André, Teije, Annette ten·2021·Applied Intelligence
DOI 10.1007/s10489-021-02394-3
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Abstract

The unification of statistical (data-driven) and symbolic (knowledge-driven) methods is widely recognized as one of the key challenges of modern AI. Recent years have seen a large number of publications on such hybrid neuro-symbolic AI systems. That rapidly growing literature is highly diverse, mostly empirical, and is lacking a unifying view of the large variety of these hybrid systems. In this paper, we analyze a large body of recent literature and we propose a set of modular design patterns for such hybrid, neuro-symbolic systems. We are able to describe the architecture of a very large number of hybrid systems by composing only a small set of elementary patterns as building blocks. The main contributions of this paper are: 1) a taxonomically organised vocabulary to describe both processes and data structures used in hybrid systems; 2) a set of 15+ design patterns for hybrid AI systems organized in a set of elementary patterns and a set of compositional patterns; 3) an application of these design patterns in two realistic use-cases for hybrid AI systems. Our patterns reveal similarities between systems that were not recognized until now. Finally, our design patterns extend and refine Kautz’s earlier attempt at categorizing neuro-symbolic architectures.

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Metadata

Authors
van Bekkum, M., de Boer, M., van Harmelen, F., Meyer-Vitali, A., & Teije, A. T.
ORCID
Venue
Applied Intelligence
Publisher
Springer Science and Business Media LLC
Published
2021-06-18
Volume
51
Issue
9
Pages
6528-6546
ISSN
  • 0924-669X
  • 1573-7497
DOI
10.1007/s10489-021-02394-3
Cited by
98
References
5
Language
en
Licence
Funders
Ministerie van Onderwijs, Cultuur en Wetenschap (024.004.022)

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