Earlier work on machine learning for automated reasoning mostly relied on simple, syntactic features combined with sophisticated learning techniques. Using ideas adopted in the software verification community, we propose the investigation of more complex, structural features to learn from. These may be exploited to either learn beneficial strategies for tools, or build a portfolio solver that chooses the most suitable tool for a given problem. We present some ideas for features of term rewrite systems and theorem proving problems.

中文翻译：

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更智能的功能，更简单的学习？

用于自动推理的机器学习的早期工作主要依赖于简单的句法特征与复杂的学习技术相结合。使用软件验证社区中采用的思想，我们建议研究更复杂的结构特征以供学习。可以利用这些来学习工具的有益策略，或者构建一个组合求解器，为给定的问题选择最合适的工具。我们针对术语重写系统的特征和定理证明问题提出了一些想法。