Date: | 2021, July 6 |
Time: | 10:00 a. m. |
Place: | Online |
Author: | Mossakowski, Till |
Title: | Modular Design Patterns for Neural-Symbolic Integration |
The talk presents the paper “Modular design patterns for hybrid learning and reasoning systems”, Applied Intelligence, June 2021.
Abstract of the paper:
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.