A Neuro-vector-symbolic Architecture for Solving Raven's Progressive Matrices

• URL: http://arxiv.org/abs/2203.04571v1
• Date: Wed, 9 Mar 2022 08:29:21 GMT
• Title: A Neuro-vector-symbolic Architecture for Solving Raven's Progressive Matrices
• Authors: Michael Hersche, Mustafa Zeqiri, Luca Benini, Abu Sebastian, Abbas Rahimi
• Abstract summary: Neuro-vector-symbolic architecture (NVSA) is proposed to combine the best of deep neural networks and symbolic logical reasoning. We show that NVSA achieves a new record of 97.7% average accuracy in RAVEN, and 98.8% in I-RAVEN datasets, with two orders of magnitude faster execution than the symbolic logical reasoning on CPUs.
• Score: 15.686742809374024
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neither deep neural networks nor symbolic AI alone have approached the kind of intelligence expressed in humans. This is mainly because neural networks are not able to decompose distinct objects from their joint representation (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 fixed-width holographic vectorized representations that serve as a common language between neural networks and symbolic logical reasoning. The efficacy of NVSA is demonstrated by solving the Raven's progressive matrices. NVSA achieves a new record of 97.7% average accuracy in RAVEN, and 98.8% in I-RAVEN datasets, with two orders of magnitude faster execution than the symbolic logical reasoning on CPUs.

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