The lifted dynamic junction tree algorithm (LDJT) efficiently answers filtering and prediction queries for probabilistic relational temporal models by building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps. Unfortunately, a non-ideal elimination order can lead to groundings even though a lifted run is possible for a model. We extend LDJT (i) to identify unnecessary groundings while proceeding in time and (ii) to prevent groundings by delaying eliminations through changes in a temporal first-order cluster representation. The extended version of LDJT answers multiple temporal queries orders of magnitude faster than the original version.

翻译：解除的动态接合树算法(LDJT)通过建立和再利用知识库的第一阶组群,用于多个查询和时间步骤,有效地回答关于概率关系时间模型的过滤和预测询问。不幸的是,非理想的消除命令可能导致停工,即使一个模型可以取消运行。我们扩大了LDJT (i) 以在进行时查明不必要的地基,以及 (ii) 通过改变时间第一阶群群群群群群群群群群群群来推迟消除,从而防止停工。LDJT的扩大版本可以比原版更快地回答多个时间级的询问。