Neuro-Symbolic Forward Reasoning
Hikaru Shindo, Devendra Singh Dhami, Kristian Kersting

TL;DR
The paper introduces NSFR, a neuro-symbolic reasoning framework combining differentiable forward-chaining with object-centric deep learning to enhance logical inference from raw data.
Contribution
It presents a novel neuro-symbolic approach that integrates differentiable forward-chaining with object-centric representations for reasoning tasks.
Findings
Effective on object-centric reasoning datasets
Outperforms existing methods in accuracy
Demonstrates versatility across 2D and 3D tasks
Abstract
Reasoning is an essential part of human intelligence and thus has been a long-standing goal in artificial intelligence research. With the recent success of deep learning, incorporating reasoning with deep learning systems, i.e., neuro-symbolic AI has become a major field of interest. We propose the Neuro-Symbolic Forward Reasoner (NSFR), a new approach for reasoning tasks taking advantage of differentiable forward-chaining using first-order logic. The key idea is to combine differentiable forward-chaining reasoning with object-centric (deep) learning. Differentiable forward-chaining reasoning computes logical entailments smoothly, i.e., it deduces new facts from given facts and rules in a differentiable manner. The object-centric learning approach factorizes raw inputs into representations in terms of objects. Thus, it allows us to provide a consistent framework to perform the…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Explainable Artificial Intelligence (XAI) · Topic Modeling
