Autonomous unmanned systems have become an attractive vehicle for a myriad of military and civilian applications. This can be partly attributed to their ability to bring payloads for utility, sensing, and other uses for various applications autonomously. However, a key challenge in realizing autonomous unmanned systems is the ability to perform complex group missions, which require coordination and collaboration among multiple platforms. This paper presents a cooperative navigating task approach that enables multiple unmanned surface vehicles (multi-USV) to autonomously capture a maneuvering target while avoiding both static and dynamic obstacles. The approach adopts a hybrid multi-agent deep reinforcement learning framework that leverages heuristic mechanisms to guide the group mission learning of the vehicles. Specifically, the proposed framework consists of two stages. In the first stage, navigation subgoal sets are generated based on expert knowledge, and a goal selection heuristic model based on the immune network model is used to select navigation targets during training. Next, the selected goals’ executions are learned using actor-critic proximal policy optimization. The simulation results with multi-USV target capture show that the proposed approach is capable of abstracting and guiding the unmanned vehicle group coordination learning and achieving a generally optimized mission execution.

Multilevel association rules mining is an important domain to discover interesting relations between data elements with multiple levels abstractions. Most of the existing algorithms toward this issue are based on exhausting search methods such as Apriori, and FP‐growth. However, when they are applied in the big data applications, those methods will suffer for extreme computational cost in searching association rules. To expedite multilevel association rules searching and avoid the excessive computation, in this paper, we proposed a novel genetic‐based method with three key innovations. First, we use the category tree to describe the multilevel application data sets as the domain knowledge. Then, we put forward a special tree encoding schema based on the category tree to build the heuristic multilevel association mining algorithm. As the last part of our design, we proposed the genetic algorithm based on the tree encoding schema that will greatly reduce the association rule search space. The method is especially useful in mining multilevel association rules in big data related applications. We test the proposed method with some big datasets, and the experimental results demonstrate the effectiveness and efficiency of the proposed method in processing big data. Moreover, our results also manifest that the algorithm is fast convergent with a limited termination threshold.

The modeling and design of multi-agent systems is imperative for applications in the evolving intelligence of unmanned systems. In this paper, we propose a multi-agent system design that is used to build a system for training a team of unmanned surface vehicles (USVs) where no historical data concerning the behavior is available. In this approach, agents are built as the physical controller of each USV and their cooperative decisions used for the USVs’ group coordination. To make our multi-agent system intelligently coordinate USVs, we built a multi-agent-based learning system. First, an agent-based data collection platform is deployed to gather competition data from agents’ observation for on-line learning tasks. Second, we design a genetic-based fuzzy rule training algorithm that is capable of optimizing agents’ coordination decisions in an accumulated manner. The simulation results of this study demonstrate that our proposed training approach is feasible and able to converge to a stable action selection policy towards efficient multi-USVs’ cooperative decision making.

Building efficient distributed coordination algorithms is critical for the large scale multiagent system design, and the communication network has been shown as a key factor to influence system performance even under the same coordination protocol. Although many distributed algorithm designs have been proved to be feasible to build their functions in the large scale multiagent systems as claimed, the performances may not be stable if the multiagent networks were organized with different complex network topologies. For example, if the network was recovered from the broken links or disfunction nodes, the network topology might have been shifted. Therefore, their influences on the overall multiagent system performance are unknown. In this paper, we have made an initial effort to find how a standard network recovery policy, MPLS algorithm, may change the network topology of the multiagent system in terms of network congestion. We have established that when the multiagent system is organized as different network topologies according to different complex network attributes, the network shifts in different ways. Those interesting discoveries are helpful to predict how complex network attributes influence on system performance and in turn are useful for new algorithm designs that make a good use of those attributes.

To build scalable and lightweight wireless sensor networks with energy saving devices, 6LoWPAN-based protocols become emergent technologies that successfully constructed ubiquitous networks and seamlessly connected the traditional Internet with WSNs. However, the existing researches cannot effectively maintain the routing and the network topology for the problems of low cost sensors' malfunctions. In this paper, we improved the HiLow protocol in aspects of interconnection between the Internet and 6LoWPAN and designed a network routing path recovery approach. In addition, we designed and modified the address mapping table in gateway to store and map nodes' EUI-64 address and 16-bit short address. Nodes from the Internet can access the 6LoWPAN nodes by an IPv6 address, which is generated by the EUI-64 address and IPv6 prefix. Due to the benefits of additional data structure and our designs, the simulation results manifested that our protocol improves the routing efficiency and can effectively reduce the cost for network reorganization caused by node failures and mobiles.

Limited communication resources have gradually become a critical factor toward efficiency of decentralized large scale multi-agent coordination when both system scales up and tasks become more complex. In current researches, due to the agent’s limited communication and observational capability, an agent in a decentralized setting can only choose a part of channels to access, but cannot perceive or share global information. Each agent’s cooperative decision is based on the partial observation of the system state, and as such, uncertainty in the communication network is unavoidable. In this situation, it is a major challenge working out cooperative decision-making under uncertainty with only a partial observation of the environment. In this paper, we propose a decentralized approach that allows agents cooperatively search and independently choose channels. The key to our design is to build an up-to-date observation for each agent’s view so that a local decision model is achievable in a large scale team coordination. We simplify the Dec-POMDP model problem, and each agent can jointly work out its communication policy in order to improve its local decision utilities for the choice of communication resources. Finally, we discuss an implicate resource competition game, and show that, there exists an approximate resources access tradeoff balance between agents. Based on this discovery, the tradeoff between real-time decision-making and the efficiency of cooperation using these channels can be well improved.