PIE-APT: Abductive Planning over Temporal Dynamic Knowledge Graphs via Incremental Reasoning
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 dec...
| Authors | Amir Hossein Sharafi, Alireza Shahbazi |
|---|---|
| Primary category | cs.AI |
| Published | 29 July 2026 |
| Last updated at source | 2 September 2026 |
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