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Learning to solve arithmetic problems with a virtual abacus

2023-01-17 13:25:52
Flavio Petruzzellis, Ling Xuan Chen, Alberto Testolin
arXiv_AI
           
Abstract

Acquiring mathematical skills is considered a key challenge for modern Artificial Intelligence systems. Inspired by the way humans discover numerical knowledge, here we introduce a deep reinforcement learning framework that allows to simulate how cognitive agents could gradually learn to solve arithmetic problems by interacting with a virtual abacus. The proposed model successfully learn to perform multi-digit additions and subtractions, achieving an error rate below 1% even when operands are much longer than those observed during training. We also compare the performance of learning agents receiving a different amount of explicit supervision, and we analyze the most common error patterns to better understand the limitations and biases resulting from our design choices.

Abstract (translated)

URL

https://arxiv.org/abs/2301.06870

PDF

https://arxiv.org/pdf/2301.06870.pdf

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