Softened Symbol Grounding for Neuro-symbolic Systems
Zenan Li, Yuan Yao, Taolue Chen, Jingwei Xu, Chun Cao, Xiaoxing Ma,, Jian L\"u

TL;DR
This paper introduces a novel softened symbol grounding method for neuro-symbolic systems, effectively bridging neural network training and symbolic reasoning to improve learning efficiency and problem-solving capabilities.
Contribution
It proposes a new framework that models symbol states with a Boltzmann distribution, uses an innovative MCMC sampling technique, and incorporates an annealing mechanism to enhance symbol grounding.
Findings
Successfully solves complex neuro-symbolic tasks beyond existing methods
Improves symbol grounding efficiency and effectiveness
Demonstrates superior performance on three benchmark tasks
Abstract
Neuro-symbolic learning generally consists of two separated worlds, i.e., neural network training and symbolic constraint solving, whose success hinges on symbol grounding, a fundamental problem in AI. This paper presents a novel, softened symbol grounding process, bridging the gap between the two worlds, and resulting in an effective and efficient neuro-symbolic learning framework. Technically, the framework features (1) modeling of symbol solution states as a Boltzmann distribution, which avoids expensive state searching and facilitates mutually beneficial interactions between network training and symbolic reasoning;(2) a new MCMC technique leveraging projection and SMT solvers, which efficiently samples from disconnected symbol solution spaces; (3) an annealing mechanism that can escape from %being trapped into sub-optimal symbol groundings. Experiments with three representative…
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Taxonomy
TopicsCognitive Science and Education Research · Neural Networks and Applications · Evolutionary Algorithms and Applications
