Learning to Solve Constraint Satisfaction Problems with Recurrent Transformer

Zhun Yang, Adam Ishay, Joohyung Lee

Keywords: transformer, constraint reasoning, semi-supervised learning

TL;DR: We show how Recurrent Transformer can be trained to perform logical reasoning on constraint satisfaction problems in an end-to-end manner and how to inject discrete constraints into its training

Abstract: Constraint satisfaction problems (CSPs) are about finding values of variables that satisfy the given constraints. We show that Transformer extended with recurrence is a viable approach to learning to solve CSPs in an end-to-end manner, having clear advantages over state-of-the-art methods such as Graph Neural Networks, SATNet, and some neuro-symbolic models. With the ability of Transformer to handle visual input, the proposed Recurrent Transformer can straightforwardly be applied to visual constraint reasoning problems while successfully addressing the symbol grounding problem. We also show how to leverage deductive knowledge of discrete constraints in the Transformer's inductive learning to achieve sample-efficient learning and semi-supervised learning for CSPs.

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