Logic could be learned from images
Authors: Qian Guo, Yuhua Qian, Xinyan Liang, Yanhong She, Deyu Li, Jiye Liang
Abstract:
Logic reasoning is a signifcant ability of human intelligence and also an important task in artifcial 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, frst 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 difcult 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.
Keywords： Logic reasoning; Data concept logic; LiLi task; Reasoning patterns
Logic could be learned from images.pdf
Fri Dec 24 19:30:00 CST 2021