Unmanned aerial vehicles (UAVs) are becoming more and more widely used in battlefield reconnaissance and target strikes because of their high cost-effectiveness, but task planning for large-scale UAV swarms is a problem that needs to be solved. To solve the high-risk problem caused by incomplete information for the combat area and the potential coordination between targets when a heterogeneous UAV swarm performs reconnaissance and strike missions, this paper proposes a distributed task-allocation algorithm. The method prioritizes tasks by evaluating the swarm’s capability superiority to tasks to reduce the search space, uses the time coordination mechanism and deterrent maneuver strategy to reduce the risk of reconnaissance missions, and uses the distributed negotiation mechanism to allocate reconnaissance tasks and coordinated strike tasks. The simulation results under the distributed framework verify the effectiveness of the distributed negotiation mechanism, and the comparative experiments under different strategies show that the time coordination mechanism and the deterrent maneuver strategy can effectively reduce the mission risk when the target is unknown. The comparison with the centralized global optimization algorithm verifies the efficiency and effectiveness of the proposed method when applied to large-scale UAV swarms. Since the distributed negotiation task-allocation architecture avoids dependence on the highly reliable network and the central node, it can further improve the reliability and scalability of the swarm, and make it applicable to more complex combat environments.

Knowledge-aware recommendation systems have shown superior performance by connecting user item interaction graph (UIG) with knowledge graph (KG) and enriching semantic connections collected by the corresponding networks. Among the existing methods, self-supervised learning has attracted the most attention for its significant effects in extracting node self-discrimination auxiliary supervision, which can largely improve the recommending rationality. However, existing methods usually employ a single (either node or edge) perspective for representation learning, over-emphasizing the pair-wise topology structure in the graph, thus overlooking the important semantic information among neighborhood-wise connection, limiting the recommendation performance. To solve the problem, we propose Hierarchical self-supervised learning for Knowledge-aware Recommendation (HKRec). The hierarchical property of the method is shown in two perspectives. First, to better reveal the knowledge graph semantic relations, we design a Triple-Graph Masked Autoencoder (T-GMAE) to force the network to estimate the masked node features, node connections, and node degrees. Second, to better align the user-item recommendation knowledge with the common knowledge, we conduct contrastive learning in a hybrid way, i.e., both neighborhood-level and edge-level dropout are adopted in a parallel way to allow more comprehensive information distillation. We conduct an in-depth experimental evaluation on three real-world datasets, comparing our proposed HKRec with state-of-the-art baseline models to demonstrate its effectiveness and superiority. Respectively, Recall@20 and NDCG@20 improved by 2.2% to 24.95% and 3.38% to 22.32% in the Last-FM dataset, by 7.0% to 23.82% and 5.7% to 39.66% in the MIND dataset, and by 1.76% to 34.73% and 1.62% to 35.13% in the Alibaba-iFashion dataset.