Graph learning is naturally well suited for use in symbolic, object-centric planning due to its ability to exploit relational structures exhibited in planning domains and to take as input planning instances with arbitrary numbers of objects. Numeric planning is an extension of symbolic planning in which states may now also exhibit numeric variables. In this work, we propose data-efficient and interpretable machine learning models for learning to solve numeric planning tasks. This involves constructing a new graph kernel for graphs with both continuous and categorical attributes, as well as new optimisation methods for learning heuristic functions for numeric planning. Experiments show that our graph kernels are vastly more efficient and generalise better than graph neural networks for numeric planning, and also yield competitive coverage performance compared to domain-independent numeric planners. Code is available at https://github.com/DillonZChen/goose

翻译：图学习天然适用于符号化、以对象为中心的规划任务，因为它能够有效利用规划领域中展现的关系结构，并能处理包含任意数量对象的规划实例。数值规划是符号规划的扩展，其中状态现在还可包含数值变量。在本研究中，我们提出了数据高效且可解释的机器学习模型，用于学习解决数值规划任务。这包括为具有连续和分类属性的图构建新的图核，以及为数值规划学习启发式函数的新优化方法。实验表明，我们的图核在数值规划中比图神经网络效率更高、泛化能力更强，并且与领域无关的数值规划器相比，也产生了具有竞争力的覆盖性能。代码可在 https://github.com/DillonZChen/goose 获取。