Defense systems are usually deployed to protect high-value targets or hot spots that are integral parts of the modern battlefield environment. However, in coastal defense operations (due to the variability of the maritime environment and the sustainability of combat), limited operational capabilities, the need for efficient coordination, and protracted combat are peculiarly challenging to meet by traditional manned fleets. In contrast, with lower costs, unmanned fleets can organize an autonomous defense against enemy targets that are capable of rapid response. This paper focuses on the typical defense scenario; we analyzed and modeled the objective functions of the intelligent defense system and propose a hierarchical distributed multi-agent-based system design scheme. Finally, to test the system’s performance, we established simulation verification experiments in a typical scenario and compared the system based on the traditional central architecture. The results show that, in a defense operation, the hierarchically-distributed multi-agent-based system shows improvements in system decision-making efficiency and interception effect.

To support Unmanned Aerial Vehicle (UAV) joint electromagnetic countermeasure decisions in real time, coordinating multiple UAVs for efficiently jamming distributed hostile radar stations requires complex and highly flexible strategies. However, with the nature of the high complexity dimension and partial observation of the electromagnetic battleground, no such strategy can be generated by pre-coded software or decided by a human commander. In this paper, an initial effort is made to integrate multiagent reinforcement learning, which has been proven to be effective in game strategy generation, into the distributed airborne electromagnetic countermeasures domain. The key idea is to design a training simulator which close to a real electromagnetic countermeasure strategy game, so that we can easily collect huge valuable training data other than in the real battle ground which is sparse and far less than sufficient. In addition, this simulator is able to simulate all the necessary decision factors for multiple UAV coordination, so that multiagents can freely search for their optimal joint strategies with our improved Independent Proximal Policy Optimization (IPPO) learning algorithm which suits the game well. In the last part, a typical domain scenario is built to test, and the use case and experiment results manifest that the design is efficient in coordinating a group of UAVs equipped with lightweight jamming devices. Their coordination strategies are not only capable of handling given jamming tasks for the dynamic jamming of hostile radar stations but also beat expectations. The reinforcement learning algorithm can do some heuristic searches to help the group find the tactical vulnerabilities of the enemies and improve the multiple UAVs’ jamming performance.

As urban populations grow, smart home technology has become a key enabler for enhancing energy efficiency, comfort, and convenience in residential environments. However, existing smart home implementations often struggle to adapt to complex and dynamic user needs due to issues like device heterogeneity, rigid preset solutions, and limited interoperability. This article presents a novel semantic-based self-configuring framework that captures the functional, operational, state, and characteristic perspectives of smart home devices, facilitating seamless interconnection and interoperability across heterogeneous devices. The proposed framework uses service entity, that is semantically modeled to act as a linker and mediator between applications and devices to enable dynamic scheduling and control based on application requests and user preferences. To enable continuous adaptation and learning, the proposed approach formulates the self-configuration problem as an Markov decision process (MDP), with parameters instantiated using semantic information and rules. A novel feedback-based reinforcement learning approach is then proposed to adjust the reward function and state transition parameters based on user feedback. The performances of the proposed approach against a bench-marked fixed and rule-based mechanisms are demonstrated through simulation.

Collaborative exploration, sensing and communication in previously unknown environments with high network latency, such as outer space, battlefields and disaster hit areas are promising in multi-agent applications. When disasters such as large fires or natural disasters occur, previously established networks might be destroyed or incapacitated. In these cases, multiple autonomous mobile robots (AMR) or autonomous unmanned ground vehicles carrying wireless devices and/or thermal sensors can be deployed to create an end-to-end communication and sensing coverage to support rescue efforts or access the severity of damage. However, a fundamental problem is how to rapidly deploy these mobile agents in such complex and dynamic environments. The uncertainties introduced by the operational environment and wide range of scheduling problem have made solving them as a whole challenging. In this paper, we present an efficient decentralized approach for practical mobile agents deployment in unknown, burnt or disaster hit areas. Specifically, we propose an approach that combines methods from Artificial Immune System (AIS) with special token messages passing for a team of interconnected AMR to decide who, when and how to act during deployment process. A distributed scheme is adopted, where each AMR makes its movement decisions based on its local observation and a special token it receives from its neighbors. Empirical evidence of robustness and effectiveness of the proposed approach is demonstrated through simulation.

In wireless connected robot swarms, information sharing is necessary for robots to cooperatively achieve joint goals under a partially observable environment. Despite significant progress on this problem, existing researchers have not adequately dealt with the case that robots broadcast messages to multiple members with a dynamically changing communication medium. In this paper, we propose an effective information sharing approach for dynamic robot teams. By using a matrix representation for information distribution, the network structure and information needs, this model allows quick reasoning through a single computation. Following our theoretical analysis, this approach can perform as the MDP model for small teams when states are fully observable. In addition, we designed heuristic algorithms to help robots adapt to dynamic network connections and information distribution in large teams. Empirical simulation results manifest that the approach performs well in uncertain environments to effectively balance sharing information with minimizing communication costs.