# Logic could be learned from images

## TL;DR

This paper introduces a new task called LiLi for learning and reasoning logic relations directly from images without predefined patterns, demonstrating the potential and challenges of neural networks in data-driven logic reasoning.

## Contribution

The study proposes the LiLi task, creates six datasets for logic reasoning from images, and develops a novel divide and conquer neural network framework to improve reasoning accuracy.

## Key findings

- Standard neural networks perform poorly on complex logic tasks.
- Adding label information significantly improves model performance.
- The LiLi datasets serve as benchmarks for visual logic reasoning.

## Abstract

Logic reasoning is a significant ability of human intelligence and also an important task in artificial intelligence. The existing logic reasoning methods, quite often, need to design some reasoning patterns beforehand. This has led to an interesting question: can logic reasoning patterns be directly learned from given data? The problem is termed as a data concept logic. In this study, a learning logic task from images, called a LiLi task, first is proposed. This task is to learn and reason the logic relation from images, without presetting any reasoning patterns. As a preliminary exploration, we design six LiLi data sets (Bitwise And, Bitwise Or, Bitwise Xor, Addition, Subtraction and Multiplication), in which each image is embedded with a n-digit number. It is worth noting that a learning model beforehand does not know the meaning of the n-digit numbers embedded in images and the relation between the input images and the output image. In order to tackle the task, in this work we use many typical neural network models and produce fruitful results. However, these models have the poor performances on the difficult logic task. For furthermore addressing this task, a novel network framework called a divide and conquer model by adding some label information is designed, achieving a high testing accuracy.