# Can We Automate Diagrammatic Reasoning?

## TL;DR

This paper introduces a new diagrammatic reasoning dataset and proposes a novel Knowledge-based LSTM model to address DR problems, marking initial progress in automating diagrammatic reasoning tasks.

## Contribution

It presents the first diagrammatic reasoning dataset and a novel KLSTM model, advancing the automation of visual reasoning beyond existing real-world object focus.

## Key findings

- KLSTM outperforms several state-of-the-art frameworks
- The dataset enables benchmarking of DR tasks
- Initial results highlight the domain's complexity

## Abstract

Learning to solve diagrammatic reasoning (DR) can be a challenging but interesting problem to the computer vision research community. It is believed that next generation pattern recognition applications should be able to simulate human brain to understand and analyze reasoning of images. However, due to the lack of benchmarks of diagrammatic reasoning, the present research primarily focuses on visual reasoning that can be applied to real-world objects. In this paper, we present a diagrammatic reasoning dataset that provides a large variety of DR problems. In addition, we also propose a Knowledge-based Long Short Term Memory (KLSTM) to solve diagrammatic reasoning problems. Our proposed analysis is arguably the first work in this research area. Several state-of-the-art learning frameworks have been used to compare with the proposed KLSTM framework in the present context. Preliminary results indicate that the domain is highly related to computer vision and pattern recognition research with several challenging avenues.