A neuro-vector-symbolic architecture for solving Raven’s progressive matrices

Neither deep neural networks nor symbolic artificial intelligence (AI) alone has approached the kind of intelligence expressed in humans. This is mainly because neural networks are not able to decompose joint representations to obtain distinct objects (the so-called binding problem), while symbolic AI suffers from exhaustive rule searches, among other problems. These two problems are still pronounced in neuro-symbolic AI, which aims to combine the best of the two paradigms. Here we show that the two problems can be addressed with our proposed neuro-vector-symbolic architecture (NVSA) by exploiting its powerful operators on high-dimensional distributed representations that serve as a common language between neural networks and symbolic AI. The efficacy of NVSA is demonstrated by solving Raven's progressive matrices datasets. Compared with state-of-the-art deep neural network and neuro-symbolic approaches, end-to-end training of NVSA achieves a new record of 87.7% average accuracy in RAVEN, and 88.1% in I-RAVEN datasets. Moreover, compared with the symbolic reasoning within the neuro-symbolic approaches, the probabilistic reasoning of NVSA with less expensive operations on the distributed representations is two orders of magnitude faster.Neuro-symbolic artificial intelligence approaches display both perception and reasoning capabilities, but inherit the limitations of their individual deep learning and symbolic artificial intelligence components. By combining neural networks and vector-symbolic architectures, Hersche and colleagues propose a neuro-vector-symbolic framework that can solve Raven's progressive matrices tests faster and more accurately than other state-of-the-art methods.