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The unification of statistical (data-driven) and symbolic (knowledge-driven)methods is widely recognised as one of the key challenges of modern AI. Recentyears have seen large number of publications on such hybrid neuro-symbolic AIsystems. That rapidly growing literature is highly diverse and mostlyempirical, and is lacking a unifying view of the large variety of these hybridsystems. In this paper we analyse a large body of recent literature and wepropose a set of modular design patterns for such hybrid, neuro-symbolicsystems. We are able to describe the architecture of a very large number ofhybrid systems by composing only a small set of elementary patterns as buildingblocks.  The main contributions of this paper are: 1) a taxonomically organisedvocabulary to describe both processes and data structures used in hybridsystems; 2) a set of 15+ design patterns for hybrid AI systems, organised in aset of elementary patterns and a set of compositional patterns; 3) anapplication of these design patterns in two realistic use-cases for hybrid AIsystems. Our patterns reveal similarities between systems that were notrecognised until now. Finally, our design patterns extend and refine Kautz'earlier attempt at categorising neuro-symbolic architectures.

Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases