Temporal Knowledge graph (TKG) reasoning aims to predict missing facts based on historical TKG data. Most of the existing methods are incapable of explicitly modeling the long-term time dependencies from history and neglect the adaptive integration of the long- and short-term information. To tackle these problems, we propose a novel method that utilizes a designed Hierarchical Relational Graph Neural Network to learn the Long- and Short-term representations for TKG reasoning, namely HGLS. Specifically, to explicitly associate entities in different timestamps, we first transform the TKG into a global graph. Based on the built graph, we design a Hierarchical Relational Graph Neural Network that executes in two levels: The sub-graph level is to capture the semantic dependencies within concurrent facts of each KG. And the global-graph level aims to model the temporal dependencies between entities. Furthermore, we design a module to extract the long- and short-term information from the output of these two levels. Finally, the long- and short-term representations are fused into a unified one by Gating Integration for entity prediction. Extensive experiments on four datasets demonstrate the effectiveness of HGLS.

With the thorough application and development of Intelligent Logistics System, the Path Planning Problem, which is one of the key problems of logistics scheduling, is accelerating to the direction of dynamics, diversification and complexity. In order to better adapt to the new changes, this paper proposes a path planning method based on improved SP-MCTS (Single-Player Monte Carlo Tree Search) algorithm. Unlike the previous heuristic algorithms, SP-MCTS does not completely depend on parameters, and can more effectively solve variable combinatorial optimization problems. Aiming at the problem of path planning, this paper enhances the simulation process of SP-MCTS by clonal selection algorithm, in order to improve the algorithm’s accuracy of locating high-potential branches, and further improve the accuracy of the decisions made by SP-MCTS. Experiments show that the method proposed in this paper is less affected by the parameters and can better solve the changing path planning problem. Compared with the existing path planning methods and original SP-MCTS algorithm, it can solve the changeable path planning problem more stably, faster and more accurately.