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Generation of Visual Representations for Multi-Modal Mathematical Knowledge

Accepted version
Peer-reviewed

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Authors

Wu, L 
Choi, S 
Raggi, D 
Stockdill, A 
Garcia, GG 

Abstract

In this paper we introduce MaRE, a tool designed to generate representations of multiple modalities for a given mathematical problem while ensuring the correctness and interpretability of the transformations between these different representations. The theoretical foundation for this tool is Representational Systems Theory (RST), a mathematical framework for studying the structure and transformations of representations. In MaRE's web front-end user interface, a set of probability equations in Bayesian Notation can be rigorously transformed into Area Diagrams, Contingency Tables, and Probability Trees with just one click, utilising a back-end engine based on RST. A table of cognitive costs, based on the cognitive Representational Interpretive Structure Theory (RIST), that a representation places on a particular profile of user is produced at the same time. MaRE is general and domain independent, applicable to other representations encoded in RST. It may enhance mathematical education and research, facilitating multi-modal knowledge representation and discovery.

Description

Keywords

46 Information and Computing Sciences, 4602 Artificial Intelligence

Journal Title

Proceedings of the AAAI Conference on Artificial Intelligence

Conference Name

The Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24)

Journal ISSN

2159-5399
2374-3468

Volume Title

38

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)
Sponsorship
EPSRC (EP/T019603/1)