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This paper investigates the stability and motion of two interactive oscillating agents. Multiple agents can be controlled in a centralized and/or distributed manner to form specific patterns in cooperative tracking, pursuit, and evasion games, as well as environmental exploration. This paper studies the behavior of two oscillating agents due to their interaction. It shows that, through a combination of selecting oscillation centers and interaction gain, a variety of motions, including limit-cycles and stationary behavior, can be realized. [ABSTRACT FROM AUTHOR]

Promoting cooperation in the network has always been a focus of evolutionary game dynamics research. In this paper, a three-strategy local decision model is designed to promote cooperative behavior between agents. The hide right is introduced to accurately describe the agents which tend to choose conservative strategy to achieve stable payoffs. Agents only consider their own and non-defective neighbors' payoffs information to make their decisions for next move. According to the adaptive dynamics, the equilibrium states under different conditions have been studied. It is found that the introduction of hide strategy helps to reduce the occurrence of defection. The conclusion of this paper will provide a reference for the cooperation promotion mechanism of selfish agents that are closer to the actual situation. [ABSTRACT FROM AUTHOR]

The emerging digitalization in today's industrial environments allows manufacturers to store online knowledge about production and use it to make better informed management decisions. This paper proposes a multi-agent framework enhanced with digital twin (DT) for production scheduling and optimization. Decentralized scheduling agents interact to efficiently manage the work allocation in different segments of production. A DT is used to evaluate the performance of different scheduling decisions and to avoid potential risks and bottlenecks. Production managers can supervise the system's decision-making processes and manually regulate them online. The multi-agent system (MAS) uses asset administration shells (AASs) for data modelling and communication, enabling interoperability and scalability. The framework was deployed and tested in an industrial pilot coming from the bicycle production industry, optimizing and controlling the short-term production schedule of the different departments. The evaluation resulted in a higher production rate, thus achieving higher production volume in a shorter time span. Managers were also able to coordinate schedules from different departments in a dynamic way and achieve early bottleneck detection. [ABSTRACT FROM AUTHOR]

This paper deals with a multi-agent system which supports the designer in solving complex design tasks. The behaviour of design agents is modelled by sets of grammar rules. Each agent uses a graph grammar or a shape grammar and a database of facts concerning the subtask it is responsible for. The course of the design process is determined by the interaction between specialised agents. Space layouts of designs are represented by attributed graphs encoding both topological structures and semantic properties of solutions. The agents work in parallel on the common graph, independently generating layouts of different design components while specified node labels evoke agents using shape grammars. The agents' cooperation allows them to combine a form-oriented approach with a functional-structural one in the design process, where the agents generate the general 3D form of the object based on design requirements together with the space layout based on the functional aspects of the solution. Based on the given design criteria, the agents search for admissible solutions within the design space that constitutes their operating environment. The proposed approach is illustrated by the example of designing kindergarten facilities. [ABSTRACT FROM AUTHOR]

Natural Language Processing (NLP) has many applications such as Speech recognition, Speech understanding, and Speech synthesis. Several approaches have been proposed in the literature in dealing with NLP. This paper describes an ongoing research project that tackles Speech Arabic Synthesis using multi-agent system techniques. The system consists of five modules (agents): the User Interface Agent (UIA), the Facilitator Agent (FA), the Preprocessing Agent (PPA), the Orthographic and Phonetic Transcription Agent, and the Speech Generation Agent. These agents are communicating with each other to construct agent sub societies representing the user input. All the agents are cognitive, work together, and communicate with the Knowledge-Base and the Sound Segments Database to generate Arabic speech signals. We used 800 Arabic sentences and asked 10 listeners with different levels of knowledge of the Arab language to accomplish the evaluation perception process. The system presents in general a Success Rate of 86% for the set of 800 tested sentences. [ABSTRACT FROM AUTHOR]

Recently, multi-agent systems have become widespread as essential technologies for various practical problems. An essential problem in multi-agent systems is collaborative automating picking and delivery operations in warehouses. The warehouse commissioning task involves finding specified items in a warehouse and moving them to a specified location using robots. This task is defined as a spatial task-allocation problem (SPATAP) based on a Markov decision process (MDP). It is considered a decentralized multi-agent system rather than a system that manages and optimizes agents in a central manner. Existing research on SPATAP involving modeling the environment as a MDP and applying Monte Carlo tree searches has shown that this approach is efficient. However, there has not been sufficient research into scenarios in which all agents are provided a common plan despite the fact that their actions are decided independently. Thus, previous studies have not considered cooperative robot behaviors with different goals, and the problem where each robot has different goals has not been studied extensively. In terms of the cooperative element, the item exchange approach has not been considered effectively in previous studies. Therefore, in this paper, we focus on the problem of each robot being assigned a different task to optimize the percentage of picking and delivering items in time in social situations. We propose an action-planning method based on the Monte Carlo tree search and an item-exchange method between agents. We also generate a simulator to evaluate the proposed methods. The results of simulations demonstrate that the achievement rate is improved in small- and medium-sized warehouses. However, the achievement rate did not improve in large warehouses because the average distance from the depot to the items increased. [ABSTRACT FROM AUTHOR]

Distribution system service restoration is a process of restoring power outages through changing the on/off status of sectionalizing and tie switches within the whole power outage in a distribution system altered accordingly with its topological structure. Multi-agent Systems (MASs) can be applied for distribution system service restoration, an engineering optimization problem that can be addressed through metaheuristics, of a distribution system because service restoration planning can be made in parallel among intelligent software agents embedded inside sectionalizing and tie switches and feeders to form a distributed multi-agent environment such that the time of power restoration can be reduced. Service restoration planning can be built upon an MAS. This paper presents a three-tiered MAS-based Multi-Population Parallel Genetic Algorithm (MPPGA) and demonstrates its preliminary implementation to achieve service restoration planning for smart distribution system service restoration, which serves as a meta service restoration planner to perform efficient and fast switching operation for smart distribution system service restoration. The presented three-tiered MAS-based MPPGA is implemented, in Java programming language, on a Java Agent DEvelopment (JADE for short) platform, where in evolutionary computation (1) multiple populations are coevolved and (2) selection, crossover, and mutation operations for genetic search are parallelized. The effectiveness/feasibility of the presented three-tiered MAS-based MPPGA is demonstrated by a simulated distribution system and reported with simulation results. [ABSTRACT FROM AUTHOR]

The study of the multi-agent task allocation problem with multiple depots is crucial for investigating multi-agent collaboration. Although many traditional heuristic algorithms can be adopted to handle the concerned task allocation problem, they are not able to efficiently obtain optimal or suboptimal solutions. To this end, a graph attention pointer network is built in this paper to deal with the multi-agent task allocation problem. Specifically, the multi-head attention mechanism is employed for the feature extraction of nodes, and a pointer network with parallel two-way selection and parallel output is introduced to further improve the performance of multi-agent cooperation and the efficiency of task allocation. Experimental results are provided to show that the presented graph attention pointer network outperforms the traditional heuristic algorithms. [ABSTRACT FROM AUTHOR]

A multi-agent system is a system of multiple interacting entities, known as intelligent agents, who possibly have different information and/or diverging interests. The agents could be robots, humans, or human teams. Opinion dynamics is a process of individual opinions in which a group of interacting agents continuously fuse their opinions on the same issue based on established rules to reach a consensus at the final stage. Historically, the idea of reaching consensus through repeated averaging was introduced by DeGroot for a structured time-invariant and synchronous environment. Since then, consensus, which is the most ubiquitous phenomenon of multi-agent systems, has become popular in various scientific fields such as biology, physics, control engineering, and social science. To some extent, a Krause mean process is a general model of opinion sharing dynamics in which the opinions are represented by vectors. In this paper, we represent opinion sharing dynamics by means of Krause mean processes generated by diagonally primitive cubic doubly stochastic matrices, and then we establish a consensus in the multi-agent system. [ABSTRACT FROM AUTHOR]

Although the formation control of multi-agent systems has been widely investigated from various aspects, the problem is still not well resolved, especially for the case of distributed output-feedback formation controller design without input information exchange among neighboring agents. Using relative output information, this paper presents a novel distributed reduced-order estimation of the formation error at a predefined time. Based on the proposed distributed observer, a neural-network-based formation controller is then designed for multi-agent systems with connected graphs. The results are verified by both theoretical demonstration and simulation example. [ABSTRACT FROM AUTHOR]

In this paper, a control strategy of a cell-based multi-agent system is proposed to solve the problem of inconsistency of series lithium-ion battery packs. The bidirectional Cuk converter is utilized as an equalizing circuit serving for balancing adjacent cells in a pack. A SOC-based consensus control with a time-triggered mechanism (TTM) is proposed. In order to reduce the actuator updates, the control method is ameliorated by altering TTM to an event-triggered mechanism (ETM). Adjustable balancing currents are designed in both TTM and ETM methods for the acceleration of the equalization process. The cases in dynamic environments under externally imposed charging/discharging currents by adopting TTM and ETM methods are investigated in detail. By comparison, the simulations and hardware-in-the-loop (HIL) experiments with a Typhoon real-time simulator are illustrated to show that, both in standby or external charging/discharging conditions, the proposed ETM algorithms are superior to TTM's in terms of equalization time and adaptability to the external environment. [ABSTRACT FROM AUTHOR]

As a major strategic component of smart protection schemes, quick and accurate fault isolation would significantly enhance the system performance. With the ever‐increasing installation of distributed generation (DG) units in the distribution networks, however, the fault isolation has become challengeable. Using the multi‐agent‐based infrastructure, this paper presents a peer‐to‐peer decentralized protection coordination scheme having the ability to quickly isolate the fault without dependency to DGs' operating points. To do so, the distribution network is first mapped into a communication network to define the relationship tables among the protection devices as the agents. Using a three‐stage algorithm, then, the faulted section is removed from the network. In this way, the agents have the ability to be informed about the operation of other agents and accordingly update their relationship tables, network configuration, short circuit level and direction of current flow. Moreover, the decentralized nature of peer‐to‐peer communication makes the approach capable of not relying on a central controller. The proposed method utilizes the common protection devices to be used as the agents and does not need additional measurement equipment. Numerical studies show that the proposed protection scheme can accurately and quickly isolate the faulted section of a high DG‐penetrated distribution network system. [ABSTRACT FROM AUTHOR]

The need for a greener and more sustainable energy system evokes a need for more extensive energy system transition research. The penetration of distributed energy resources and Internet of Things technologies facilitate energy system transition towards the next generation of energy system concepts. The next generation of energy system concepts include "integrated energy system", "multi-energy system", or "smart energy system". These concepts reveal that future energy systems can integrate multiple energy carriers with autonomous intelligent decision making. There are noticeable trends in using the agent-based method in research of energy systems, including multi-energy system transition simulation with agent-based modeling (ABM) and multi-energy system management with multi-agent system (MAS) modeling. The need for a comprehensive review of the applications of the agent-based method motivates this review article. Thus, this article aims to systematically review the ABM and MAS applications in multi-energy systems with publications from 2007 to the end of 2021. The articles were sorted into MAS and ABM applications based on the details of agent implementations. MAS application papers in building energy systems, district energy systems, and regional energy systems are reviewed with regard to energy carriers, agent control architecture, optimization algorithms, and agent development environments. ABM application papers in behavior simulation and policy-making are reviewed with regard to the agent decision-making details and model objectives. In addition, the potential future research directions in reinforcement learning implementation and agent control synchronization are highlighted. The review shows that the agent-based method has great potential to contribute to energy transition studies with its plug-and-play ability and distributed decision-making process. [ABSTRACT FROM AUTHOR]

In recent years, the development of smart cities has accelerated. There are several issues to handle in smart cities, one of the most important of which is efficient resource allocation. For the modeling of smart cities, multi-agent systems (MASs) can be used. In this paper, an efficient approach is proposed for resource allocation in smart cities based on the multi-agent credit assignment problem (MCA) and bankruptcy game. To this end, the resource allocation problem is mapped to MCA and the bankruptcy game. To solve this problem, first, a task start threshold (TST) constraint is introduced. The MCA turns into a bankruptcy problem upon introducing such a constraint. Therefore, based on the concept of bankruptcy, three methods of TS-Only, TS + MAS, and TS + ExAg are presented to solve the MCA. In addition, this work introduces a multi-score problem (MSP) in which a different reward is offered for solving each part of the problem, and we used it in our experiments to examine the proposed methods. The proposed approach is evaluated based on the learning rate, confidence, expertness, efficiency, certainty, and correctness parameters. The results reveal the better performance of the proposed approach compared to the existing methods in five parameters. [ABSTRACT FROM AUTHOR]

In this paper, a multi-agent system (MAS) based on the A-Team concept is proposed to solve the Distributed Resource-Constrained Multi-Project Scheduling Problem (DRCMPSP). In the DRCMPSP, multiple distributed projects are considered. Hence, the local task schedule for each project and a coordination of the shared decisions are considered. The DRCMPSP belongs to the class of the strongly NP-hard optimization problems. Multi-agent system seems the natural way of solving such problems. The A-Team MAS, proposed in this paper, has been built using the JABAT environment where two types of the optimization agents are used: local and global. Local optimization agents are used to find solutions for the local projects, and global optimization agents are responsible for the coordination of the local projects and for finding the global solutions. The approach has been tested experimentally using 140 benchmark problem instances from MPSPLIB library with minimizing the Average Project Delay (APD) as global optimization criterion. [ABSTRACT FROM AUTHOR]

In today's world, merging sensor-based security systems with contemporary principles has become crucial. As we witness the ever-growing number of interconnected devices in the Internet of Things (IoT), it is imperative to have robust and trustworthy security measures in place. In this paper, we examine the idea of virtualizing the communication infrastructure for smart farming in the context of IoT. Our approach utilizes a metaverse-based framework that mimics natural processes such as mycelium network growth communication with a security-concept-based srtificial immune system (AIS) and transaction models of a multi-agent system (MAS). The mycelium, a bridge that transfers nutrients from one plant to another, is an underground network (IoT below ground) that can interconnect multiple plants. Our objective is to study and simulate the mycelium's behavior, which serves as an underground IoT, and we anticipate that the simulation results, supported by diverse aspects, can be a reference for future IoT network development. A proof of concept is presented, demonstrating the capabilities of such a virtualized network for dedicated sensor communication and easy reconfiguration for various needs. [ABSTRACT FROM AUTHOR]

In structural vibration response sensing, mobile sensors offer outstanding benefits as they are not dedicated to a certain structure; they also possess the ability to acquire dense spatial information. Currently, most of the existing literature concerning mobile sensing involves human drivers manually driving through the bridges multiple times. While self-driving automated vehicles could serve for such studies, they might entail substantial costs when applied to structural health monitoring tasks. Therefore, in order to tackle this challenge, we introduce a formation control framework that facilitates automatic multi-agent mobile sensing. Notably, our findings demonstrate that the proposed formation control algorithm can effectively control the behavior of the multi-agent systems for structural response sensing purposes based on user choice. We leverage vibration data collected by these mobile sensors to estimate the full-field vibration response of the structure, utilizing a compressive sensing algorithm in the spatial domain. The task of estimating the full-field response can be represented as a spatiotemporal response matrix completion task, wherein the suite of multi-agent mobile sensors sparsely populates some of the matrix's elements. Subsequently, we deploy the compressive sensing technique to obtain the dense full-field vibration complete response of the structure and estimate the reconstruction accuracy. Results obtained from two different formations on a simply supported bridge are presented in this paper, and the high level of accuracy in reconstruction underscores the efficacy of our proposed framework. This multi-agent mobile sensing approach showcases the significant potential for automated structural response measurement, directly applicable to health monitoring and resilience assessment objectives. [ABSTRACT FROM AUTHOR]

In this paper, we consider the distributed polynomial path tracking problem for a swarm of autonomous underwater vehicles (AUVs) modeled by second-order uncertain multi-agent systems. The application scenario of this paper has three distinguished characteristics. First, the communication network for the multi-agent system is unreliable and switching. Under the jointly connected condition, the communication network can be disconnected the entire time. Second, it is supposed that only the relative position between AUVs can be obtained for trajectory tracking control. Third, the AUV dynamics are subject to uncertain system parameters. By applying the cooperative output regulation control framework, a novel distributed robust control scheme is proposed to solve the distributed path tracking problem, which consists of three parts. First, to cope with communication network uncertainty, the distributed observer was invoked to recover the polynomial path for each AUV. Second, based on the relative position measurement between AUVs, a pseudo position estimator was adopted to generate the pseudo position for each AUV. Finally, based on the estimated polynomial path and the pseudo position, a certainty equivalent robust internal model control law was synthesized to achieve asymptotic reference trajectory tracking, where the internal model compensator aims to tackle uncertain system parameters. Numerical simulations are provided to validate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

The paper introduces a proposal of an Autonomous Navigation System for Unmanned Surface Vessels. The system architecture is presented with a special emphasis on collision avoidance and maneuver auto-negotiation. For the purpose of maneuver auto-negotiation, the concept of multi-agent systems has been applied. The algorithm developed for the task of collision avoidance is briefly described and the results of the simulation tests, confirming the effectiveness of applied method, are also given. Presented outcomes include solutions of test scenarios from the perspectives of different ships taking part in the considered situations, confirming the applicability of the collision avoidance algorithm in the process of maneuver auto-negotiation. [ABSTRACT FROM AUTHOR]

Distributed optimization algorithms are widely used for solving science and engineering problems in recent years. However, these algorithms become vulnerable in the face of external attacks, so it is urgent to develop novel resilient distributed optimization algorithms against attacks. In this paper, we design a novel median-based resilient distributed optimization algorithm to defend the Byzantine attack. The existence of transition matrix is proved, and the convergence and optimality of the proposed algorithm are analyzed. Simulation experiments are performed to verify the effectiveness of the proposed algorithm and the results show that it is more effective than the compared one and can get the best possible results against the attack. [ABSTRACT FROM AUTHOR]

This paper is concerned with the secure output consensus problem for the heterogeneous multi-agent systems under the event-triggered scheme in the presence of the denial-of-service attack. Without detecting the attack, the hold-input controller update strategy is adopted when some transmission data may be lost due to the effect of the attack. Based on the tolerable duration of the attack, a novel edge-based event-triggered scheme is developed. The scheme can avoid continuous communication and exclude Zeno behavior. With the aid of the switched system theory, output consensus is preserved. An example shows the effectiveness. • Secure problem for the heterogeneous multi-agent systems is solved in the presence of the denial-of-service attack. • A novel edge-based event-triggered scheme is developed based on the tolerable duration of the attack. • Without detecting the attack, the hold-input controller update strategy is adopted. • Output consensus is preserved with the aid of the switched system theory. [ABSTRACT FROM AUTHOR]

This paper presents a novel leader-follower consensus control for a particular class of nonlinear multi-agent mechanical systems in the presence of control input constraints and external disturbances which includes robot system dynamics with a wide range of potential applications in industry. In this case, one of the agents is selected as the leader to direct the other agents in such a way that the whole system can reach consensus within certain prescribed performance transient bounds. Due to the presence of disturbances in most practical systems, the effect of limited disturbance in the consensus control method has been investigated, and actuator saturation is included in the design process. A terminal sliding mode control method has been adapted to ensure the stability of the overall system and fast finite-time leader-follower consensus control. The simulation results of the multi-agent nonlinear robot system in MATLAB environment, in different scenarios with simultaneous consideration of actuator saturation and external disturbance, will show the efficiency of the proposed control method. [ABSTRACT FROM AUTHOR]

Complexity science is a new stage in the development of systems science that is the frontier areas of contemporary scientific development. Complexity science takes complex systems as the research object, which has attracted widespread attention from researchers in the fields of economy, control, management, and society. In recent years, with the rapid development of science and technology and people's deepening understanding for the theory of complex systems, the systems are no longer an object with a single function, but the systems are composed of multiple individuals with autonomous capabilities through cooperative and cooperation, namely multi-agent system(MAS). Currently, MAS is one of the main models for studying such complex systems. The intelligent control is to break the traditional multiagent fault-tolerant control (FTC) concept and produce a new type of compensation mechanism. In this paper, the applications of fault-tolerant control methods for MASs are presented, and a discussion is given about development and challenges in this field. [ABSTRACT FROM AUTHOR]

In this paper, a distributed adaptive finite-time consensus (FTC) control protocol for a high-order multi-agent system (MAS) with intermittent communications under switching topologies is proposed. Meanwhile, considering the problem of heterogeneous unknown nonlinearities and other uncertain disturbances, the adaptive neural network and the sliding mode control method are used to compensate the nonlinearity of each agent separately. The agents are homogeneous, so the system has symmetry. The switching topologies considered in this paper are asymmetric. Compared with consensus protocol for asymptotic convergence, simulation results show that the proposed method can effectively solve the presence of the nonlinear and accelerate the convergence speed of the system so that an FTC can be reached. [ABSTRACT FROM AUTHOR]

In this paper, a time-varying formation tracking protocol for second-order multi-sgent systems (MASs) is presented. The time-varying formation considers translation, rotation, and scaling of the geometric pattern that defines the formation. The control law is simple yet effective, and it is composed of a trajectory tracking control and a consensus control that considers the position and velocity feedback of the connected agents in the MAS. The closed-loop system is asymptotically stable, and this was proved using the Gershgoring's disk theorem. The performance of the protocol was extensively tested in experiments using a dynamic extension of the differential-drive robot model. The protocol was tested for different communication topologies and also dealt with switching topologies. The proposed protocol presented good performance regaring both time-varying formation and topology changes. Moreover, a comparison with an existing controller and with only trajectory tracking control has been provided, thus showing that the proposed protocol preserves the formation for all the tested topologies in a better way. [ABSTRACT FROM AUTHOR]

Featured Application: This work combines a decentralized multi-agent system working form, using a similar approach to associative memory, focusing on the dynamic association of motion processes, to achieve clustered recognition of motion behavior by designing a multilayer structured multi-agent system. Multi-agent systems are suitable for handling complex problems due to their high parallelism and autonomous evolution ability. In this paper, we propose an adaptive clustering multi-agent learning system for intelligent applications with continuously changing requirements. Each agent model changes temporal sequences using the longest common subsequence (LCS) algorithm. Multiple agents collaborate in a multilayer decentralized approach to enhance learning adaptability and achieve self-supervised behavioral clustering. The system is constructed using a "memory-like" method and operates primarily on memory access and comparison, avoiding extensive matrix operations of artificial neural networks while achieving learning and prediction functions. We chose an unsupervised vehicle behavioral clustering scenario for feasibility validation in which the system's cognitive objective is to cluster and recognize vehicle behaviors. In a low computational environment, the system can complete clustering functions and exhibit continuous learning capabilities when new behavioral changes occur. The proposed approach achieves an accuracy of 97.4% while processing at a speed 1–5 times faster than similar clustering algorithms. The verification results indicate that this system has excellent potential to enhance intelligent sensing front ends. [ABSTRACT FROM AUTHOR]

Finite-field networks (FFNs) are a class of multi-agent systems over finite fields with sensing, computing, and communication capabilities. FFNs have been investigated extensively to save computing and communication resources. This paper summarizes the current research results to provide a direction for future research. First, different models of FFNs are reviewed, including FFNs with timedelays, switching topology, and leader-following structures. Then, the consensus and synchronization problems of multi-agent systems over finite fields are analyzed, and the necessary and sufficient conditions for consensus and synchronization of some autonomous systems have been derived in recent research. Finally, the distributed control of multi-agent systems over finite fields has been developed by many scholars based on various approaches. [ABSTRACT FROM AUTHOR]

The optimal dispatch of reactive power is a fundamental task in the operational planning of electrical power systems. This task aims to minimize active power losses and improve voltage levels within the electrical power system. This paper presents the application of the particle swarm optimization methodology to achieve optimal reactive power dispatch. The methodology's performance is demonstrated by its high processing speed and the results obtained through a comprehensive global search for reactive power dispatch. Additionally, experimental results confirm the algorithm's effectiveness in optimizing the objective function across different case studies, highlighting its ability to achieve optimal reactive power dispatch. This study represents a significant advancement in the field of power system optimization and provides a useful tool for managing and controlling these systems. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) resources' installation has numerous advantages in distribution feeders nonetheless they may threaten the selectivity and reliability of protective installations. All proposed schemes including employing distance or directional overcurrent relays, mathematics methods, online overcurrent relay settings change, fault current limiters, PV disconnection after fault, computing optimal installation location of PVs, and the others are too costly or complex or inefficient in PV-penetrated feeders. This paper proposes a new central processor unit to create a smart overcurrent relay and prohibit protection blinding, nuisance tripping, unwanted tripping, and synchronous tripping of overcurrent relay in a PV-penetrated distribution feeder. Firstly, the processor consists of four layers of the multi-agent system, which receives information on the relays and sensed current via the grid. Secondly, each agent is responsible to prevent a separate maloperation event and scrutinizing the input information. Finally, it directs the manipulated commend to the respective breaker if relay has acted incorrectly. Overcurrent relays become smart and miscoordination and maloperation problems are corrected completely by implementing the proposed processor. The proposed scheme is fast, offline, inexpensive, independent of the fault and PV installation location, type and impedance of the fault, and PV capacity. The results indicate the proposed plan has acted successfully with 100% efficiency in all scenarios. [ABSTRACT FROM AUTHOR]

This paper investigates the exponential consensus problem for a class of nonlinear leader-following multi-agent systems using impulsive control, where impulses are generated by the event-triggered mechanism and are subjected to actuation delays. It is proved that Zeno behavior can be avoided, and by employing the linear matrix inequality technique, some sufficient conditions for realizing exponential consensus of the considered system are derived. Actuation delay is an important factor affecting the consensus of the system, and our results show that increasing the actuation delay can enlarge the lower bound of the triggering interval, while it harms the consensus. To demonstrate the validity of the obtained results, a numerical example is provided. [ABSTRACT FROM AUTHOR]

The design of agents interacting with human beings is becoming a crucial problem in many real-life applications. Different methods have been proposed in the research areas of human–computer interaction (HCI) and multi-agent systems (MAS) to model teams of participants (agents and humans). It is then necessary to build models analyzing their decisions when interacting, while taking into account the specificities of these interactions. This paper, therefore, aimed to propose an explicit model of such interactions based on game theory, taking into account, not only environmental characteristics (e.g., criticality), but also human characteristics (e.g., workload and experience level) for the intervention (or not) of agents, to help the latter. Game theory is a well-known approach to studying such social interactions between different participants. Existing works on the construction of game matrices required different ad hoc descriptors, depending on the application studied. Moreover, they generally focused on the interactions between agents, without considering human beings in the analysis. We show that these descriptors can be classified into two categories, related to their effect on the interactions. The set of descriptors to use is thus based on an explicit combination of all interactions between agents and humans (a weighted sum of 2-player matrices). We propose a general model for the construction of game matrices based on any number of participants and descriptors. It is then possible to determine using Nash equilibria whether agents decide (or not) to intervene during the tasks concerned. The model is also evaluated through the determination of the gains obtained by the different participants. Finally, we illustrate and validate the proposed model using a typical scenario (involving two agents and two humans), while describing the corresponding equilibria. [ABSTRACT FROM AUTHOR]

In modern smart manufacturing, robots are often connected via a network, and their task can change according to a predetermined program. Iterative learning control (ILC) is widely used for robots executing high-precision operations. Under network conditions, the efficiency of ILC algorithms may decrease if the program is restructured. In particular, the learning error may temporarily increase to an unacceptable value when changing the reference trajectory. This paper considers a networked system with the following features: the reference trajectory and parameters change between passes according to a known program, agents are subjected to random disturbances, and measurements are carried out with noise. In addition, the network topology changes due to the disconnection of some agents from the network and the connection of new agents to the network according to a given program. A distributed ILC design method is proposed based on vector Lyapunov functions for repetitive processes in combination with Kalman filtering. This method ensures the convergence of the learning error and reduces its increase caused by changes in the reference trajectory and network topology. The effectiveness of the proposed method is confirmed by an example. [ABSTRACT FROM AUTHOR]

This paper mainly studies the consensus of a new multi-agent system which can heuristically construct the communication network topology. Firstly, we presented some preparatory knowledge and constructed a dynamic model for the new multi-agent system. Secondly, we studied the control protocol. Inspired by the laws of nature, we designed some strategies for constructing the communication network topology heuristically, and these strategies were used in the control protocol. At the same time, in order to save the cost of communication and control, the impulsive control mechanism was also used in the control protocol. Thirdly, based on the dynamic model and the control protocol, we derived a state error system. Further, we used Lyapunov stability theory to analyze the state error system and obtained two theorems that enable the new multi-agent system to achieve consensus. Finally, we compared the proposed model with several related studies. Meanwhile, we also applied the results we obtained into the real scenario of symmetric secure communication to verify their practicability and efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the pattern formation of objects that can be stacked and transported by distributed autonomous agents. Inspired by the social behavior of termite colonies, which often build elaborate three-dimensional structures (nest towers), this paper explores the mechanism of termite-like agents through a computational and minimalistic approach. We introduce a cellular automata model (i.e., spatially discretized) for the agents and the objects they can transport, where each agent follows a "rule" determined by the assignment of fundamental actions (move/ load/ unload) based on the state of its neighboring cells. To evaluate the resulting patterns from the viewpoint of structural complexity and agent effort, we classify the patterns using the Kolmogorov dimension and higher-order local autocorrelation, two well-known statistical techniques in image processing. We find that the Kolmogorov dimension provides a good metric for the structural complexity of a pattern, whereas the higher-order local autocorrelation is an effective means of identifying particular local patterns. [ABSTRACT FROM AUTHOR]

This paper investigates the consensus of multi-agent systems (MASs) by virtue of eventtriggered mechanism. Considering the existence of external disturbances, we use a disturbance observer to estimate the disturbance signals and eliminate the corresponding effects by using estimators to compensate the input control terms. The self-triggered condition is designed and proved that there is no Zeno behavior. We show that the proposed disturbance observer can estimate the external disturbance signals well under the self-triggered condition. Finally, simulation examples are presented to verify the theoretical results. [ABSTRACT FROM AUTHOR]

This paper addresses the leader–follower consensus control problem for a nonlinear multi-agent robot system with control input constraint and external disturbances. Robot system is one of the most important practical systems in the industry. Due to the presence of disturbances in most practical systems, this paper considers the issue of finite-time leader–follower consensus control of the nonlinear multi-agent robot system along with actuator saturation and bounded disturbance. The modified terminal sliding mode control method is suggested for the system which is able to guarantee the stability of the overall system and fast finite-time leader–follower consensus control. For two different scenarios, the simulation of multi-agent robot system has been performed. The results show the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of designing secure control for networked multi-agent systems (MASs) under Denial-of-Service (DoS) attacks. We propose a constructive design method based on the interaction topology. The MAS with a non-attack communication topology, modeled by quasi-Abelian Cayley graphs subject to DoS attacks, can be represented as a switched system. Using switching theory, we provide easily implementable sufficient conditions for the networked MAS to remain asymptotically stable despite DoS attacks. Our results are applicable to both continuous-time and discrete-time systems, as well as to discrete-time systems with variable steps or systems that combine discrete and continuous times. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the algorithms for traversal exploration and path coverage of target regions using multiple agents, enabling the efficient deployment of a set of agents to cover a complex region. First, the original multi-agent path planning problem (mCPP) is transformed into several single-agent sub-problems, by dividing the target region into multiple balanced sub-regions, which reduces the explosive combinatorial complexity; subsequently, closed-loop paths are planned in each sub-region by the rapidly exploring random trees (RRT) algorithm to ensure continuous exploration and repeated visits to each node of the target region. On this basis, we also propose two improvements: for the corner case of narrow regions, the use of geodesic distance is proposed to replace the Eulerian distance in Voronoi partitioning, and the iterations for balanced partitioning can be reduced by more than one order of magnitude; the Dijkstra algorithm is introduced to assign a smaller weight to the path cost when the geodesic direction changes, which makes the region division more "cohesive", thus greatly reducing the number of turns in the path and making it more robust. The final optimization algorithm ensures the following characteristics: complete coverage of the target area, wide applicability of multiple area shapes, reasonable distribution of exploration tasks, minimum average waiting time, and sustainable exploration without any preparation phase. [ABSTRACT FROM AUTHOR]

This paper investigates the multi-agent interception problem under switching topology based on deep reinforcement learning. Due to communication restrictions or network attacks, the connectivity between every two intercepting agents may change during the entire tracking process before the successful interception. That is, the topology of the multi-agent system is switched, which leads to a partial missing or dynamic jump of each agent's observation. To solve this issue, a novel multi-agent level-fusion actor-critic (MALFAC) approach is proposed with a direction assisted (DA) actor and a dimensional pyramid fusion (DPF) critic. Besides, an experience adviser (EA) function is added to the learning process of the actor. Furthermore, a reward factor is proposed to balance the relationship between individual reward and shared reward. Experimental results show that the proposed method performs better than recent algorithms in the multi-agent interception scenarios with switching topologies, which achieves the highest successful interception with the least average steps. The ablation study also verifies the effectiveness of the innovative components in the proposed method. The extensive experimental results demonstrate the scalability of our method in different scenarios. [ABSTRACT FROM AUTHOR]

Interactions and dynamics are critical mechanisms for multi-agent systems to achieve complex intelligence through the cooperation of simple agents. Yet, inferring interactions of the multi-agent system is still a common and open problem. A new method named K-similarity is designed to measure the global relative similarities for inferring the interactions among multiple agents in this paper. K-similarity is defined to be a synthetic measure of relative similarity on each observation snapshot where regular distances are nonlinearly mapped into a network. Therefore, K-similarity contains the global relative similarity information, and the interaction topology can be inferred from the similarity matrix. It has the potential to transform into distance strictly and detect multi-scale information with various K strategies. Therefore, K-similarity can be flexibly applied to various synchronized dynamical systems with fixed, switching, and time-varying topologies. In the experiments, K-similarity outperforms four benchmark methods in accuracy in most scenarios on both simulated and real datasets, and shows strong stability towards outliers. Furthermore, according to the property of K-similarity we develop a Gaussian Mixture Model (GMM)-based threshold to select probable interactions. Our method contributes to not only similarity measurement in multi-agent systems, but also other global similarity measurement problems. [ABSTRACT FROM AUTHOR]

This study investigates the use of interactions as a metric to detect emergent phenomena in the ongoing agent-based simulations. The proposed algorithm aims to detect the flocking behavior in the Boids model. Boids model is simple to understand and to use and always verifies emergence. In recent years, several metrics have been proposed to investigate emergent behaviors in complex systems. Two main limitations of these studies are, how to choose a proper metric to avoid a high coast of computation, and how to detect emergence as it happens, i.e., live detection. In this paper, we propose an interaction-based algorithm to detect emergent behavior in Boids model. The three main contributions of this work are: (1) using interaction as metric, (2) detect emergence when it happens, (3) no need to store simulation data for post-mortem analysis. A prior interactions analysis was done to classify the system interactions. The results show that the algorithm was able to detect the rise of the flocking, i.e., emergence in the Boids model. Simulation results are provided to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

With the advancement in the technology, deployment of sensors in the industrial or public building is increasing rapidly. The basic aim is to obtain the data from the environment and decision making to the energy saving. The activities caused by the human results the undergoing negative change in the environment. There are many techniques available for decision making and consider the environmental factors solely which cause the energy consumption. However, user's preferences are not adapted by the systems, but at energy consumption optimization, these systems are very successful. The end-users use the system which considers the factors and their wellbeing are get affected. The distributed generation is incorporated by the Smart Small Grid (SSG), communication network and the sensors for the more reliable, flexible and efficient grid. The energy saving system is presented in this paper which also adapts to the inhabitants preferences apart from environmental conditions consideration. The architecture of Multi-Agent System (MAS) and the agents are utilized for negotiation process performance between the users comfort preferences and optimization degree that according to these preferences, achievement of system is done. The energy consumption of 40% is obtained and in the inhabitants' behavior pattern, the algorithm was specialized. The 16.89% of reduction is obtained by the existing system and it was focused to obtain the agreement between the system and users for user preference satisfaction and the energy optimization is also performed at the same time. [ABSTRACT FROM AUTHOR]

In this paper, a layered, undirected-network-structure, optimization approach is proposed to reduce the redundancy in multi-agent information synchronization and improve the computing rate. Based on the traversing binary tree and aperiodic sampling of the complex delayed networks theory, we proposed a network-partitioning method for pinning dynamic networks, with a more simplified, analyzable structure, and all of the traversed nodes are mathematically asymptotically synchronized at the same time. Moreover, a systematic implementable approach is proposed for application. The approach could be separated into two main steps: the first is a method of network partition that reduces the trivial interaction, which does not affect the information traversal, and the second involves applying the time-dependent functional approach of Lyapunov to give global exponential conditions, under the criteria for the synchronization of multiple agents, with a lower conservatism of the decision variables compared to some other results, so that the information available could fully benefit from the actual discrete-time communication pattern. Both mathematical proofs and numerical example evidence are presented to demonstrate the effectiveness of the implemented approach. This class contains a number of practically interesting systems, for instance, unmanned aerial vehicle (UAV) formation systems or the ground-air coordinated unmanned aerial system. [ABSTRACT FROM AUTHOR]

• This paper studies the leader-following flocking problem of the unmanned aerial vehicle (UAV). • It employs hybrid event-triggered control and pinning mechanism with delayed control inputs. • Criteria on flocking stability and Zeno behaviour prevention are derived. • It combines both displacement-based control and gyroscopic forces. • The tolerance of network instability in terms of continuous part is increased. This paper studies the leader-following flocking control problem of unmanned aerial vehicle (UAV) systems by integrating a hybrid impulsive framework, where the full exchange of information only occurs at each impulsive time instant. It is also shown that the overall stability can be achieved via impulses while the continuous dynamics remains destabilizing. Meanwhile, the even-triggered and pinning mechanisms are considered to further reduce control resource usage and transmission redundancy. Moreover, topology switching and strong nonlinearity are taken into account for more practical application. Based on transmission topology structure, impulsive control theory, and Lyapunov based event-triggered strategy, some improved theorems are derived to guarantee convergence of the corresponding error dynamics without exhibiting Zeno behaviour. Compared with most existing results on continuous or impulsive systems, the obtained stability criteria are more general as they are applicable to systems involving both delayed coupling feedback and delayed impulses. In addition, a novel approach using braking and gyroscopic forces is utilized for collision avoidance purposes. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

Multi-Agent systems consist of multiple interacting agents that solve complex problems too difficult for monolithic systems to solve. Each agent has a different role and is not aware of the state of other agents. Multi-Agent Reinforcement Learning is a common approach to training agents in these systems. Many researchers tend to focus on the implementation of reinforcement learning for training agents towards a particular goal. This paper, in contrast, implements reinforcement learning for a different purpose. This paper is about the analysis of emergent cooperative strategies between agents while achieving their goals. The research will be done using simulations of a cooperative game and documenting the strategies the agents create over a set number of episodes. [ABSTRACT FROM AUTHOR]

In this paper, neurodynamic approaches are proposed for solving nonsmooth distributed optimization problems under inequality and set constraints, that is to find the solution that minimizes the sum of local cost functions. A continuous-time neurodynamic approach is designed and its state solution exists globally and converges to an optimal solution of the corresponding distributed optimization problem. Then, a neurodynamic approach with event-triggered mechanism is considered for the purpose of saving communication costs, and then, the convergence and its Zeno-free property are proved. Moreover, to realize the practical application of the neurodynamic approach, a discrete-time neurodynamic approach is proposed to solve nonsmooth distributed optimization problems under inequality and set constraints. It is rigorously proved that the iterative sequence generated by the discrete-time neurodynamic approach converges to the optimal solution set of the distributed optimization problem. Finally, numerical examples are solved to demonstrate the effectiveness of the proposed neurodynamic approaches, and the neurodynamic approach is further applied to solve the ill-conditioned Least Absolute Deviation problem and the load sharing optimization problem. [ABSTRACT FROM AUTHOR]