Neuro-symbolic AI

As argued by Leslie Valiant[1] and others,[2][3] the effective construction of rich computational cognitive models demands the combination of symbolic reasoning and efficient machine learning.

Both are needed for a robust, reliable AI that can learn, reason, and interact with humans to accept advice and answer questions.

Such dual-process models with explicit references to the two contrasting systems have been worked on since the 1990s, both in AI and in Cognitive Science, by multiple researchers.

[10] Henry Kautz's taxonomy of neuro-symbolic architectures[11] follows, along with some examples: These categories are not exhaustive, as they do not consider multi-agent systems.

[11] Recently, Sepp Hochreiter argued that Graph Neural Networks "...are the predominant models of neural-symbolic computing"[16] since "[t]hey describe the properties of molecules, simulate social networks, or predict future states in physical and engineering applications with particle-particle interactions.