We present a new methodology for utilising machine learning technology in symbolic computation research. We explain how a well known human-designed heuristic to make the choice of variable ordering in cylindrical algebraic decomposition may be represented as a constrained neural network. This allows us to then use machine learning methods to further optimise the heuristic, leading to new networks of similar size, representing new heuristics of similar complexity as the original human-designed one. We present this as a form of ante-hoc explainability for use in computer algebra development.

翻译：我们提出了一种在符号计算研究中利用机器学习技术的新方法。我们解释了如何将圆柱代数分解中用于选择变量排序的著名人工设计启发式方法表示为约束神经网络。这使得我们能够利用机器学习方法进一步优化该启发式方法，从而生成类似规模的新网络，这些网络代表了与原始人工设计启发式方法复杂度相近的新型启发式规则。我们将其视为一种适用于计算机代数开发的先验可解释性形式。