Generation of Visual Representations for Multi-Modal Mathematical Knowledge - Wu, Lianlong; Choi, Seewon; Raggi, Daniel; Stockdill, Aaron; Garcia Garcia, Grecia; Colarusso, Fiorenzo; Cheng, Peter C.H.; Jamnik, Mateja (Proceedings of the AAAI Conference on Artificial Intelligence, 2024)
In this paper we introduce MaRE, a tool designed to generate representations in multiple modalities for a given mathematical problem while ensuring the correctness and interpretability of the transformations between 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.