用一致性更新语义融合本体知识，提升自动规划的推理能力
Automated planning with ontologies under coherence update semantics (Extended Version)
- 结合本体动作条件与一致性更新的动作效果，实现更精准的规划
- 复杂度与现有方法相当，且可通过多项式编译转化为经典规划
- 适合需要背景知识融合的智能系统规划场景
传统自动规划使用一阶逻辑公式在封闭世界语义下，从初始状态通过给定动作达成目标。本文研究将背景知识（如本体）引入规划问题的方法，通常在开放世界语义下解释本体。我们提出一种基于DL-Lite本体的新规划方法，融合了显式输入知识和动作基（eKABs）提供的本体动作条件，以及在一致性更新语义下的本体感知动作效果。结果表明，该形式化方法的复杂度不高于以往方法，并提供了一种通过多项式编译到经典规划的实现方式。对现有及新基准的评估检验了不同编译变体在规划系统上的性能表现。
原文摘要 · Abstract (English)
Standard automated planning employs first-order formulas under closed-world semantics to achieve a goal with a given set of actions from an initial state. We follow a line of research that aims to incorporate background knowledge into automated planning problems, for example, by means of ontologies, which are usually interpreted under open-world semantics. We present a new approach for planning with DL-Lite ontologies that combines the advantages of ontology-based action conditions provided by explicit-input knowledge and action bases (eKABs) and ontology-aware action effects under the coherence update semantics. We show that the complexity of the resulting formalism is not higher than that of previous approaches and provide an implementation via a polynomial compilation into classical planning. An evaluation of existing and new benchmarks examines the performance of a planning system on different variants of our compilation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。