Two-stage Rule-induction Visual Reasoning on RPMs with an Application to Video Prediction

Wang, He; Ren, Jiadong; Bai, Ruibin; Jiang, Xudong

doi:10.48550/arxiv.2111.12301

Cited by 1 publication

(1 citation statement)

References 45 publications

(113 reference statements)

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“…Neuro-symbolic approach and the next approach are data-driven approach, whereas the first two approaches are knowledge-based approaches. Examples of neuro-symbolic models for solving RPM-like tasks include ALANS2, PrAE, VAE-GPP, TRIVR, LoGe, and NVSA (Zhang et al, 2020(Zhang et al, , 2021Shi et al, 2021;He et al, 2021;Yu et al, 2021;Hersche et al, 2022).…”

Section: Neuro-symbolic Approachmentioning

confidence: 99%

Deep Non-Monotonic Reasoning for Visual Abstract Reasoning Tasks

Yang¹,

Sanyal²,

Michelson³

et al. 2023

Preprint

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While achieving unmatched performance on many welldefined tasks, deep learning models have also been used to solve visual abstract reasoning tasks, which are relatively less well-defined, and have been widely used to measure human intelligence. However, current deep models struggle to match human abilities to solve such tasks with minimum data but maximum generalization. One limitation is that current deep learning models work in a monotonic way, i.e., treating different parts of the input in essentially fixed orderings, whereas people repeatedly observe and reason about the different parts of the visual stimuli until the reasoning process converges to a consistent conclusion, i.e., non-monotonic reasoning. This paper proposes a non-monotonic computational approach to solve visual abstract reasoning tasks. In particular, we implemented a deep learning model using this approach and tested it on the RAVEN dataset-a dataset inspired by the Raven's Progressive Matrices test. Results show that the proposed approach is more effective than existing monotonic deep learning models, under strict experimental settings that represent a difficult variant of the RAVEN dataset problem.

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