Abstract Reasoning via Logic-guided Generation

Abstract

Abstract reasoning, i.e., inferring complicated patterns from given observations, is a central building block of artificial general intelligence. While humans find the answer by either eliminating wrong candidates or first constructing the answer, prior deep neural network (DNN)-based methods focus on the former discriminative approach. This paper aims to design a framework for the latter approach and bridge the gap between artificial and human intelligence. To this end, we propose logic-guided generation (LoGe), a novel generative DNN framework that reduces abstract reasoning as an optimization problem in propositional logic. LoGe is composed of three steps: extract propositional variables from images, reason the answer variables with a logic layer, and reconstruct the answer image from the variables. We demonstrate that LoGe outperforms the black box DNN frameworks for generative abstract reasoning under the RAVEN benchmark, i.e., reconstructing answers based on capturing correct rules of various attributes from observations.

arXiv commentICML 2021 Workshop on Self-Supervised Learning for Reasoning and Perception (Spotlight Talk)

References in corpus (8)

• Neural Discrete Representation Learning
• Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
• Measuring abstract reasoning in neural networks
• Learning Perceptual Inference by Contrasting
• Abstract Reasoning with Distracting Features
• The Scattering Compositional Learner: Discovering Objects, Attributes, Relationships in Analogical Reasoning
• The Mixing method: low-rank coordinate descent for semidefinite programming with diagonal constraints
• Attention on Abstract Visual Reasoning