Ontologies are known for their ability to organize rich metadata, support the identification of novel insights via semantic queries, and promote reuse. In this paper, we consider the problem of automated planning, where the objective is to find a sequence of actions that will move an agent from an initial state of the world to a desired goal state. We hypothesize that given a large number of available planners and diverse planning domains; they carry essential information that can be leveraged to identify suitable planners and improve their performance for a domain. We use data on planning domains and planners from the International Planning Competition (IPC) to construct a planning ontology and demonstrate via experiments in two use cases that the ontology can lead to the selection of promising planners and improving their performance using macros - a form of action ordering constraints extracted from planning ontology. We also make the planning ontology and associated resources available to the community to promote further research.

翻译：本体以其组织丰富元数据、通过语义查询支持新见解识别以及促进复用的能力而闻名。本文研究自动规划问题，其目标是在给定初始世界状态和期望目标状态的情况下，寻找一组能使智能体从初始状态迁移至目标状态的动作序列。我们假设：在存在大量可用规划器和多样化规划领域的前提下，这些规划器与领域本身携带着关键信息，可被利用来识别合适的规划器并提升其在特定领域中的性能。我们使用国际规划竞赛（IPC）中的规划领域和规划器数据构建规划本体，并通过两个用例实验证明：该本体能够引导选择有前景的规划器，并利用从规划本体中提取的宏操作（一种动作排序约束形式）提升其性能。我们还向学术界公开规划本体及相关资源，以促进进一步研究。