A program to create new geometry proof problems

IF 1.2 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Annals of Mathematics and Artificial Intelligence Pub Date : 2023-05-16 DOI:10.1007/s10472-023-09854-1
Philip Todd, Danny Aley
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Abstract

In a previous paper Todd (Submitted to AMAI, 2022), linear systems corresponding to sets of angle bisector conditions are analyzed. In a system which is not full rank, one bisector condition can be derived from the others. In that paper, we describe methods for finding such rank deficient linear systems. The vector angle bisector relationship may be interpreted geometrically in a number of ways: as an angle bisector, as a reflection, as an isosceles triangle, or as a circle chord. A rank deficient linear system may be interpreted as a geometry theorem by mapping each vector angle bisector relationship onto one of these geometrical representations. In Todd (Submitted to AMAI, 2022) we illustrate the step from linear system to geometry theorem with a number of by-hand constructed examples. In this paper, we present an algorithm which automatically generates a geometry theorem from a starting point of a linear system of the type identified in Todd (Submitted to AMAI, 2022). Both statement and diagram of the new theorem are generated by the algorithm. Our implementation creates a simple text description of the new theorem and utilizes the Mathematica GeometricScene to form a diagram.

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创建新的几何证明问题的程序
在之前的一篇论文Todd(提交给AMAI, 2022)中,分析了与角平分线条件集合对应的线性系统。在非满秩系统中,一个平分线条件可以由其他条件导出。在这篇文章中,我们描述了寻找这类缺秩线性系统的方法。矢量角平分线关系可以用多种几何方式解释:作为角平分线,作为反射,作为等腰三角形,或作为圆弦。通过将每个向量角平分线关系映射到这些几何表示之一上,可以将秩亏线性系统解释为一个几何定理。在Todd(提交给AMAI, 2022)中,我们用一些手工构造的例子说明了从线性系统到几何定理的步骤。在本文中,我们提出了一种算法,该算法从Todd(提交给AMAI, 2022)中确定的类型的线性系统的起点自动生成几何定理。该算法生成了新定理的表述和图解。我们的实现为新定理创建了一个简单的文本描述,并利用Mathematica GeometricScene来形成一个图表。
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来源期刊
Annals of Mathematics and Artificial Intelligence
Annals of Mathematics and Artificial Intelligence 工程技术-计算机:人工智能
CiteScore
3.00
自引率
8.30%
发文量
37
审稿时长
>12 weeks
期刊介绍: Annals of Mathematics and Artificial Intelligence presents a range of topics of concern to scholars applying quantitative, combinatorial, logical, algebraic and algorithmic methods to diverse areas of Artificial Intelligence, from decision support, automated deduction, and reasoning, to knowledge-based systems, machine learning, computer vision, robotics and planning. The journal features collections of papers appearing either in volumes (400 pages) or in separate issues (100-300 pages), which focus on one topic and have one or more guest editors. Annals of Mathematics and Artificial Intelligence hopes to influence the spawning of new areas of applied mathematics and strengthen the scientific underpinnings of Artificial Intelligence.
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