Multirobot information gathering in adversarial environments, where enemies can destroy detected robots, presents unique challenges not addressed by existing algorithms designed for safe settings. The advent of multirobot systems enables collaborative risk-avoidance behavior, exemplified in pursuit-evasion scenarios, where concentrated robot groups enhance their ability to evade and overcome pursuing enemies. However, current multirobot information gathering algorithms have not yet integrated this collaborative risk-avoidance model, potentially leading to robot damage and reduced efficiency. This study adopts a decomposition-and-grafting mechanism to separate the problem into two weakly coupled subproblems: task execution and task allocation. For task execution, we propose an exact algorithm based on the branch-and-pricing method. Our task execution algorithms are seamlessly integrated with a novel task map composition algorithm designed to identify high-utility solutions with minimal computational overhead, addressing the task allocation problem. Extensive simulations demonstrate that our algorithms significantly outperform benchmarks by increasing the number of collaborative risk-avoidance activities conducted by robots during information gathering tasks in adversarial environments. This research advances multirobot information gathering by incorporating collaborative risk-avoidance, enhancing robot survivability and efficiency in hazardous settings.

With the high degree of integration of production capacity in the industrial field, the original form of single, linear, and vertical cooperation between different industrial chains has been broken, and a multiplex networked industrial chain has been formed. Traditional time series forecasting methods are often prone to fall into the trap of computational volume caused by long historical information and the problem of dimensional explosion caused by the mixing of redundant information in the face of multiple networked industrial chain capacity forecasting with large data volume and high information dimensionality. In this paper, we first propose an information decoupling technique based on the principle of time series decomposition to provide more accurate cyclical forecasting results for capacity forecasting. Secondly, this paper introduces a multi-scale dynamic aggregation network technique. This technique dynamically aggregates and predicts variables at different time scales. The combination of these two approaches is adept at capturing a wider range of local and global trends, thereby greatly improving the accuracy and robustness of forecasting models. In this paper, experiments are conducted to compare with the current mainstream time series prediction algorithms. The results show that in multivariate long time series, the error of our algorithm is reduced by 27.8%.

Demand response has recently become an essential means for businesses to reduce production costs in industrial chains. Meanwhile, the current industrial chain structure has also become increasingly complex, forming new characteristics of multiplex networked industrial chains. Fluctuations in real-time electricity prices in demand response propagate through the coupling and cascading relationships within and among these network layers, resulting in negative impacts on the overall energy management cost. However, existing demand response methods based on reinforcement learning typically focus only on individual agents without considering the influence of dynamic factors on intra and inter-network relationships. This paper proposes a Layered Temporal Spatial Graph Attention (LTSGA) reinforcement learning algorithm suitable for demand response in multiplex networked industrial chains to address this issue. The algorithm first uses Long Short-Term Memory (LSTM) to learn the dynamic temporal characteristics of electricity prices for decision-making. Then, LTSGA incorporates a layered spatial graph attention model to evaluate the impact of dynamic factors on the complex multiplex networked industrial chain structure. Experiments demonstrate that the proposed LTSGA approach effectively characterizes the influence of dynamic factors on intra- and inter-network relationships within the multiplex industrial chain, enhancing convergence speed and algorithm performance compared with existing state-of-the-art algorithms.

With the advancement of electronic information technology and the growth of the intelligent industry, the industrial sector has undergone a shift from simplex, linear, and vertical chains to complex, multi-level, and multi-dimensional networked industrial chains. In order to enhance energy efficiency in multiplex networked industrial chains under time-of-use price, a coarse time granularity task scheduling approach has been adopted. This approach adjusts the distribution of electricity supply based on task deadlines, dividing it into longer periods to facilitate batch access to task information. However, traditional simplex-network task assignment optimization methods are unable to achieve a globally optimal solution for cross-layer links in multiplex networked industrial chains. Existing solutions struggle to balance execution costs and completion efficiency in time-of-use price scenarios. Therefore, this paper presents a mixed-integer linear programming model for solving the problem scenario and two algorithms: an exact algorithm based on the branch-and-bound method and a multi-objective heuristic algorithm based on cross-layer policy propagation. These algorithms are designed to adapt to small-scale and large-scale problem scenarios under coarse time granularity. Through extensive simulation experiments and theoretical analysis, the proposed methods effectively optimize the energy and time costs associated with the task execution.

Recently, with the increasing complexity of multiplex Unmanned Aerial Vehicles (multi-UAVs) collaboration in dynamic task environments, multi-UAVs systems have shown new characteristics of inter-coupling among multiplex groups and intra-correlation within groups. However, previous studies often overlooked the structural impact of dynamic risks on agents among multiplex UAV groups, which is a critical issue for modern multi-UAVs communication to address. To address this problem, we integrate the influence of dynamic risks on agents among multiplex UAV group structures into a multi-UAVs task migration problem and formulate it as a partially observable Markov game. We then propose a Hybrid Attention Multi-agent Reinforcement Learning (HAMRL) algorithm, which uses attention structures to learn the dynamic characteristics of the task environment, and it integrates hybrid attention mechanisms to establish efficient intra- and inter-group communication aggregation for information extraction and group collaboration. Experimental results show that in this comprehensive and challenging model, our algorithm significantly outperforms state-of-the-art algorithms in terms of convergence speed and algorithm performance due to the rational design of communication mechanisms.