Distributed Multi-agent Systems Research Articles

Minimum‐energy time‐varying formation of distributed multi‐agent systems with interaction constraints

AbstractIn the current paper, minimum‐energy time‐varying formation design and analysis problems of distributed multi‐agent systems (DMASs) subjected to randomly switching topologies and aperiodic interaction pauses are investigated, which can ensure the time‐varying formation achievement with the minimum‐energy consumption in the sense of the linear matrix inequality. Firstly, a formation control protocol is designed with local information of neighboring agents, where the total energy consumption during the formation process is constructed and the impacts of interaction constraints are imposed. Then, minimum‐energy formation design and analysis criteria of DMASs are presented in terms of the linear matrix inequality tool, which can be checked by the generalized eigenvalue method. Finally, a numerical simulation is shown to verify the effectiveness of the main results.

Finite-Time Group Consensus of Distributed Multiagent Systems Based on Competition Mechanism

With the development of communication technology, distributed cooperative controls of multiagent systems (MAS) have been applied in many fields. Based on the communication between the agents, multiple individual systems are composed of a distributed network. Since the function of the agents is different, the distributed network can be divided into many parts. For first-order/second-order dynamic MAS, group cooperation algorithms with the competition mechanism are proposed in this paper. By applying modern control theory and homogeneous theory, finite-time stabilities for the cooperation motion of dynamic MAS are analyzed. By studying the dynamic properties of agents, finite-time group consensus of distributed MAS based on directed topology is obtained. Finally, the system simulation results are given to illustrate the effectiveness of the conclusion.

Stable Consensus Decision Making for Spatially Distributed Multiagent Systems with Multiple Leaders

This paper considers the consensus decision-making problem of spatially distributed multiagent systems with multiple leaders, where the leaders have the preference about the destination, while the followers have no such preference. The agents have limited capability to sense the movement information of their local neighbors determined via the spatial relative distance, and make movement decisions accordingly. The objective is to guide the followers to move with the same preferred direction as leaders have (i.e., reaching a stable consensus). We provide detailed analysis for the information transfer between the subgroups of leaders and followers, and establish quantitative results on the proportion of leaders needed to reach the stable consensus for two cases: leaders with the same preference and leaders with different preferences. For the system where the leaders have the same preference, we provide an upper bound (impossibility theorem) and a lower bound (sufficient condition) for the proportion of leaders needed in reaching the stable consensus. When the leaders have two different preferences, we provide a sufficient condition and a necessary condition on the proportions of two subgroups of leaders to achieve stable consensus decisions.

A Novel Deep Learning Approach for Semantic Interoperability Between Heteregeneous Multi-Agent Systems

This article focuses on the issue of semantic interoperability in heterogeneous distributed multi-agent systems. Existing middleware technologies offer programming models that strongly combine agents’ learning models and communication models, which can lead to performance weaknesses when the number of agents is very important. Moreover, existing methods in the field of semantic interoperability solve the problem of understanding messages exchanged between distributed agents with heterogeneous ontologies, using several techniques to combine these ontologies. The first category of these methods relies on the fusion principle, others use alignment, and finally, there are those founded on Semantic Web technique. All these methods are limited to abstract concepts and do not deal with concrete concepts such as those represented by images. We propose in this paper a new approach that addresses the problem of semantic interoperability between heterogeneous distributed agents based on two principles: At first, the communication aspect of the agent from the learning aspect is separated. Then, we propose extending semantic interoperability to concrete concepts by combining two techniques: Semantic Web technology, which allows terms representing abstract concepts to be interpreted and deep learning technology, which is introduced as a new method to ensure semantic interoperability in the case of concrete concepts such as images. A detailed description of the proposed approach is provided, showing that it is very useful in solving the disadvantages of existing multi-agent platforms.

Resilient Autonomous Control of Distributed Multiagent Systems in Contested Environments.

An autonomous and resilient controller is proposed for leader-follower multiagent systems under uncertainties and cyber-physical attacks. The leader is assumed nonautonomous with a nonzero control input, which allows changing the team behavior or mission in response to the environmental changes. A resilient learning-based control protocol is presented to find optimal solutions to the synchronization problem in the presence of attacks and system dynamic uncertainties. An observer-based distributed H∞ controller is first designed to prevent propagating the effects of attacks on sensors and actuators throughout the network, as well as to attenuate the effect of these attacks on the compromised agent itself. Nonhomogeneous game algebraic Riccati equations are derived to solve the H∞ optimal synchronization problem and off-policy reinforcement learning (RL) is utilized to learn their solution without requiring any knowledge of the agent's dynamics. A trust-confidence-based distributed control protocol is then proposed to mitigate attacks that hijack the entire node and attacks on communication links. A confidence value is defined for each agent based solely on its local evidence. The proposed resilient RL algorithm employs the confidence value of each agent to indicate the trustworthiness of its own information and broadcast it to its neighbors to put weights on the data they receive from it during and after learning. If the confidence value of an agent is low, it employs a trust mechanism to identify compromised agents and remove the data it receives from them from the learning process. The simulation results are provided to show the effectiveness of the proposed approach.

Unreliable communication in high-performance distributed multi-agent systems: A ingenious scheme in high computing

Designing of distributed consensus algorithms featuring accuracy, robustness, reliability, and speed of convergence is in high demand for various multi-agent applications. In this research, it has been investigated to device a novel design of distributed estimation algorithm which can tackle the problem of unreliable communication among multi-agents to achieve consensus on the average value of their initial values and must be capable of computing the total number of agents in the system under dynamically changing interaction topologies. A dynamically changing network topology is considered in this research with unreliable communication links, and four different scenarios are established to be analyzed for the proposed consensus-based distributed estimation algorithm. This study established a consensus for a dynamically changing interaction topology among agents, for addition of agents in the network with dynamically switching topology at any instant in communication, for removal of agents from the network with dynamically switching topology at any instant in communication, and for a fixed topology with link failure and a reconnection with the same agent after each iteration. The proposed algorithm paces up the rate of convergence by reducing the number of iteration, along with sure convergence of the designed algorithm using the concepts of stochastic differential equation theory, control system theory, algebraic graph theory, and algebraic matrix theory. Finally, in the end, simulation results are provided which are clear evidence to validate the effectiveness of theoretical results of the proposed algorithm in comparison to previously known consensus algorithms in terms of different performance parameters.

Asynchronous Communication under Reliable and Unreliable Network Topologies in Distributed Multiagent Systems: A Robust Technique for Computing Average Consensus

Nearly all applications in multiagent systems demand precision, robustness, consistency, and rapid convergence in designing of distributed consensus algorithms. Keeping this thing in our sight, this research suggests a robust consensus protocol for distributed multiagent networks, continuing asynchronous communications, where agent’s states values are updated at diverse interval of time. This paper presents an asynchronous communication for both reliable and unreliable network topologies. The primary goal is to delineate local control inputs to attain time synchronization by processing the update information received by the agents associated in a communication topology. Additionally in order to accomplish the robust convergence, modelling of convergence analysis is conceded by commissioning the basic principles of graph and matrix theory alongside the suitable lemmas. Moreover, statistical examples presenting four diverse scenarios are provided in the end; produced results are the recognisable indicator to authenticate the robust effectiveness of the proposed algorithm. Likewise, a simulation comparison of the projected algorithm with the other existing approaches is conducted, considering different performance parameters are being carried out to support our claim.

Psychology-Inspired Trust Restoration Framework in Distributed Multiagent Systems

Trust violation during cooperation of autonomous agents in multiagent systems is usually unavoidable and can arise due to a wide number of reasons. From a psychological point of view, the violation of an agent’s trust is a result of one agent (which is a transgressor) expressing a very low weight on the welfare of another agent (which is a victim) by inflicting a high cost for a very small benefit. In order for the victim to make an effective decision about whether to cooperate or punish for the next interaction, a psychological variable called welfare tradeoff ratio (WTR) can be used to upregulate the transgressor’s disposition so that the number of exploitive behaviors that are likely to happen in the future will be decreased. In this paper, we propose computational models of metrics based on the welfare tradeoff ratio along with the way by which multiple metrics can be integrated to provide the final result. Additionally, a number of experiments based on social network analysis are conducted to evaluate the performance of the proposed framework and the results show that by implementing WTR the simulated network is able to deal with different levels of trust violation effectively.

Adaptation, coordination, and local interactions via distributed approachability

This paper investigates the relation between cooperation, competition, and local interactions in large distributed multi-agent systems. The main contribution is the game-theoretic problem formulation and solution approach based on the new framework of distributed approachability, and the study of the convergence properties of the resulting game model. Approachability theory is the theory of two-player repeated games with vector payoffs, and distributed approachability is here presented for the first time as an extension to the case where we have a team of agents cooperating against a team of adversaries under local information and interaction structure. The game model turns into a nonlinear differential inclusion, which after a proper design of the control and disturbance policies, presents a consensus term and an exogenous adversarial input. Local interactions enter into the model through a graph topology and the corresponding graph-Laplacian matrix. Given the above model, we turn the original questions on cooperation, competition, and local interactions, into convergence properties of the differential inclusion. In particular, we prove convergence and exponential convergence conditions around zero under general Markovian strategies. We illustrate our results in the case of decentralized organizations with multiple decision-makers.

Sequential Nonlinear Learning for Distributed Multiagent Systems via Extreme Learning Machines.

We study online nonlinear learning over distributed multiagent systems, where each agent employs a single hidden layer feedforward neural network (SLFN) structure to sequentially minimize arbitrary loss functions. In particular, each agent trains its own SLFN using only the data that is revealed to itself. On the other hand, the aim of the multiagent system is to train the SLFN at each agent as well as the optimal centralized batch SLFN that has access to all the data, by exchanging information between neighboring agents. We address this problem by introducing a distributed subgradient-based extreme learning machine algorithm. The proposed algorithm provides guaranteed upper bounds on the performance of the SLFN at each agent and shows that each of these individual SLFNs asymptotically achieves the performance of the optimal centralized batch SLFN. Our performance guarantees explicitly distinguish the effects of data- and network-dependent parameters on the convergence rate of the proposed algorithm. The experimental results illustrate that the proposed algorithm achieves the oracle performance significantly faster than the state-of-the-art methods in the machine learning and signal processing literature. Hence, the proposed method is highly appealing for the applications involving big data.

Advances on intelligent network management

A traditional paradox in the management of communications networks is the centralisation or distribution of control. A network is said to have centralized control when one node takes all the decisions. Problems arise when the network is geographically distributed, and the central node has to make decisions with an incomplete and possibly outdated knowledge. Also, a link failure could cause the isolation of part of the network, or if the central node itself fails then the whole network could become inoperable. Moreover, there have been a number of emerging technologies (e.g., cloud and grid computing) for supporting various information communication. The needs on autonomous and intelligent management of computer networks is ever-increasing. Thus, there should be intelligent methodologies and frameworks to control them efficiently and effectively. Particularly, in a large-scale distributed system, agents and multi-agent systems are related to a modern software paradigm which has long been recognized as a promising technology for constructing and managing complex, autonomous and intelligent systems. Furthermore, in the context of economic perspective, it is necessary to take into account the cost of communication and managements.

Structured P2P technologies for distributed command and control

The utility of Peer-to-Peer (P2P) systems extends far beyond traditional file sharing. This paper provides an overview of how P2P systems are capable of providing robust command and control for Distributed Multi-Agent Systems (DMASs). Specifically, this article presents the evolution of P2P architectures to date by discussing supporting technologies and applicability of each generation of P2P systems. It provides a detailed survey of fundamental design approaches found in modern large-scale P2P systems highlighting design considerations for building and deploying scalable P2P applications. The survey includes unstructured P2P systems, content retrieval systems, communications structured P2P systems, flat structured P2P systems and finally Hierarchical Peer-to-Peer (HP2P) overlays. It concludes with a presentation of design tradeoffs and opportunities for future research into P2P overlay systems.

AgentService: a framework to develop distributed multiagent systems

This paper presents AgentService, a framework for developing Multiagent Systems (MAS). The key features introduced with AgentService are a flexible agent model and a modular platform. Agents can perform multitask activities as well as move among different platforms. The MAS is hosted in a modular environment so that the platform can be customised. Several tools come with the programming suite, in particular: a visual protocol designer and a set of language extensions integrated into a professional development environment to ease development.

A layered architecture for real-time distributed multi-agent systems

Real-time computations in open distributed systems have functional as well as coordination requirements. Specifically, distributed agents may require to be coordinated to satisfy real-time and other quality of service (QoS) constraints. However, this coordination is difficult to achieve because of the unpredictability of computational resource availability in an open system.A three-layered architecture for computations in an open distributed multi-agent system is presented, which keeps functional, coordination and resource concerns of an application separate, allowing each to be studied separately. Functional requirements of the computation are pursued by a system of primitive agents called actors. Timing constraints are imposed on these agents through meta-operations carried out by coordinators. Resource encapsulations called cyberorgs are used for creating execution environments for agents and coordinators with predictable availability of resources.Programming constructs are developed for implementing cyber-orgs and coordinators.

Clustered volatility in multiagent dynamics

Large distributed multiagent systems are characterized by vast numbers of agents trying to gain access to limited resources in an unpredictable environment. Agents in these systems continuously switch strategies in order to opportunistically find improvements in their utilities. We analyzed the fluctuations around equilibrium that arise from strategy switching and discovered the existence of a new phenomenon. It consists of the appearance of sudden bursts of activity that punctuate the fixed point, and is due to an effective random walk consistent with overall stability. This clustered volatility is followed by relaxation to the fixed point but with different strategy mixes from the previous one. This bursting phenomenon is the result of the system continually changing between strategy configurations that are consistent with the overall equilibrium.