发表机构
Najm; Tafresh University（纳吉姆; 塔夫雷什大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出统一框架PIE-APT，含PIE-Abducer与PIE-APT模块，通过增量直接推导溯因解决动态知识图谱规划与矛盾检测问题，在OWL基准上优于经典规划器与MHS基线。
AI 中文摘要
对动态知识图谱（DKG）进行推理与规划面临重大挑战，尤其是在信息不完整的开放世界环境中。现有动作形式化方法常面临可判定性问题与派生问题，而通过结构溯因管理不完整知识需要庞大的组合搜索。本文提出一个包含两个集成模块的统一框架——PIE-Abducer（增量直接推导溯因）与PIE-APT（时态知识图谱溯因规划），二者原生适用于高表达力的描述逻辑。我们将沿线性时间线的状态转换建模为对演绎封闭的描述逻辑理论的非单调更新。将增量推理器视为黑盒，并在OWL中原生表示动作而不使用外部模态算子，可保持逻辑可判定性。为解决不完整知识问题，PIE-Abducer规避了传统的最小击中集（MHS）枚举，不采用组合句法搜索，而是将目标的逻辑否定注入一致分支，并通过直接反驳后果提取缺失前提。随后，PIE-APT采用递归“生成-测试”架构，将反向链A*搜索与PIE-Abducer交错进行至有界因果深度，再通过正向链时态投影进行严格验证。我们评估了四个OWL基准，这些基准强调了经典规划中缺失的语义能力：带见证搜索的参数化目标、搜索中的描述逻辑蕴涵、开放世界假设注入以及对抗性矛盾检测。结果表明，该框架在质量上优于经典规划器，且我们的直接推导方法在溯因丰富过程中，在数量上优于忠实于MHS的基线。
英文摘要
Planning over Temporal Dynamic Knowledge Graphs (TDKGs) presents theoretical challenges in open-world environments with incomplete information. Existing action formalisms often face decidability issues and the Ramification Problem, while structural abduction requires expansive combinatorial search spaces. We introduce a unified framework with two modules--PIE-Abducer (incremental direct-derivation abduction) and PIE-APT (Abductive Planning for TDKGs)--operating natively on the expressive SROIQ Description Logic. Modeling state transitions as non-monotonic updates to deductively closed DL theories, we represent actions natively in OWL. This leverages an incremental reasoner to preserve decidability and natively bypass the Ramification Problem. To address incomplete knowledge, PIE-Abducer circumvents Minimal Hitting Set (MHS) enumeration. Instead of combinatorial search, it injects the logical negation of a goal into a consistent DL branch and synthesizes missing premises via direct refutation consequences. PIE-APT employs a recursive Generate-and-Test architecture, interleaving backward-chaining A* search with PIE-Abducer to synthesize both action sequences and abductive assumptions. Candidates undergo strict validation via forward-chaining Temporal Projection to evaluate logical trajectories. We evaluate four OWL benchmarks targeting semantic abilities missing from classical planning: parameterized goals with witness search, mid-search DL entailment, open-world assumption injection, and adversarial plan synthesis. Results show qualitative superiority over classical planners and prove our direct-derivation approach significantly outperforms an MHS-faithful baseline in abductive enrichment.
