Computer Algebra Systems (e.g. Maple) are used in research, education, and industrial settings. One of their key functionalities is symbolic integration, where there are many sub-algorithms to choose from that can affect the form of the output integral, and the runtime. Choosing the right sub-algorithm for a given problem is challenging: we hypothesise that Machine Learning can guide this sub-algorithm choice. A key consideration of our methodology is how to represent the mathematics to the ML model: we hypothesise that a representation which encodes the tree structure of mathematical expressions would be well suited. We trained both an LSTM and a TreeLSTM model for sub-algorithm prediction and compared them to Maple's existing approach. Our TreeLSTM performs much better than the LSTM, highlighting the benefit of using an informed representation of mathematical expressions. It is able to produce better outputs than Maple's current state-of-the-art meta-algorithm, giving a strong basis for further research.

计算机代数系统在研究、教育和工业领域中得到了广泛应用。本研究通过使用机器学习来引导子算法的选择，并通过训练TreeLSTM模型表明其在数学表达式表示方面的优势，能够产生比传统方法更好的输出结果，为进一步研究提供了坚实的基础。

使用机器学习进行符号积分算法选择：LSTMs vs Tree LSTMs