Proceedings of the International Florida Artificial Intelligence Research Society Conference (May 2024)

Lattice-Based Generation of Euclidean Geometry Figures

  • Jonathan Henning,
  • Hanna King,
  • Sophie Ngo,
  • Jake Shore,
  • Alex Gardner,
  • Chris Alvin,
  • Grace Stadnyk

DOI
https://doi.org/10.32473/flairs.37.1.135297
Journal volume & issue
Vol. 37

Abstract

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We present a user-guided method to generate geometry figures appropriate for high school Euclidean geometry courses: a useful starting point for an intelligent tutoring system to provide meaningful, realistic figures for study. We first establish that a two-dimensional geometry figure can be represented abstractly using a complete, lattice we call a geometry figure lattice (GFL). As input, we take a user-defined vector of primitive geometry shapes and convert each into a GFL. We then exhaustively combine each these ‘primitive’ GFLs into a set of complex GFLs using a process we call gluing. We mitigate redundancy in GFLs by introducing a polynomial-time algorithm for determining if two GFLs are isomorphic. These lattices act as a template for the second step: instantiating GFLs into a sequence of concrete geometry figures. To identify figures that are structurally similar to textbook problems, we use a discriminator model trained on a corpus of textbook geometry figures.

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