Lifted inference allows to perform inference in polynomial time w.r.t. domain sizes. For a lifted algorithm, completeness investigates model classes for which the algorithm is guaranteed to compute a lifted solution. We contribute, to the best of our knowledge, the first completeness and complexity analysis for a temporal lifted algorithm, the so-called lifted dynamic junction tree algorithm (LDJT). To treat time as a first class citizen, LDJT introduces some constraints. Given these constraints, we analyse the classes of liftable models. Further, we show that LDJT has many advantages from a complexity point of view compared to a propositional temporal inference algorithm w.r.t. domain sizes. Therefore, LDJT advances the number of models for which inference tasks can be solved in reasonable time not only from a practically point of view, but also from a theoretical point of view.

翻译：取消的推论允许在多角度时间 w.r.t.域大小中进行推断。 对于解除的算法,完整性调查了算法保证能计算解除的解算法的模型类别。我们根据我们的知识,为暂时取消的算法提供了第一个完整性和复杂性分析,即所谓的取消的动态连接树算法(LDJT)。为了将时间作为第一等级公民对待,LDJT引入了一些限制。鉴于这些限制,我们分析了可升动模型的类别。此外,我们从复杂的角度来看,LDJT与假设的时间推论算法的 w.r.t. 域大小相比有许多好处。因此,LDJT不仅从实际的角度,而且从理论的角度,推算出可以合理时间解决推理任务的模型数量。