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Research questionHow can planning in temporal dynamic knowledge graphs handle missing facts and action ramifications while preserving decidability?Open-world temporal graphs may omit facts needed to achieve or explain a goal, while actions can produce indirect consequences through logical entailments. Planning must combine temporal updates with abductive completion without making reasoning undecidable or search prohibitively combinatorial.
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Latest papersRecent research connected to this question, newest first.PIE-APT: Abductive Planning over Temporal Dynamic Knowledge Graphs via Incremental ReasoningThe source presents PIE-APT and PIE-Abducer for SROIQ Description Logic and OWL-native actions, using incremental reasoning, recursive plan search, and temporal projection for validation. Evidence comes from four OWL benchmarks covering witness search, mid-search entailment, open-world assumption injection, and adversarial plan synthesis; the reported comparisons are against classical planners and an MHS-faithful abductive baseline.research paper · Sep 2, 2026
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