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This study proposes a modification of the GeoCity model previously developed by the authors, detailing the age structure of the population, personal schedule on weekdays and working days, and individual health characteristics of the agents. This made it possible to build a more realistic model of the functioning of the city and its residents. The developed model made it possible to simulate the spread of three types of strain of the COVID-19 virus, and to analyze the adequacy of this model in the case of unhindered spread of the virus among city residents. Calculations based on the proposed model show that SARS-CoV 2 spreads mainly from contacts in workplaces and transport, and schoolchildren and preschool children are the recipients, not the initiators of the epidemic. The simulations showed that fluctuations in the dynamics of various indicators of the spread of SARS-CoV 2 were associated with the difference in the daily schedule on weekdays and weekends. The results of the calculations showed that the daily schedules of people strongly influence the spread of SARS-CoV 2. Under assumptions of the model, the results show that for the more contagious "rapid" strains of SARS-CoV 2 (omicron), immunocompetent people become a significant source of infection. For the less contagious "slow strains" (alpha) of SARS-CoV 2, the most active source of infection is immunocompromised individuals (pregnant women). The more contagious, or "fast" strain of the SARS-CoV 2 virus (omicron), spreads faster in public transport. For less contagious, or "slow" strains of the virus (alpha), the greatest infection occurs due to work and educational contacts. [ABSTRACT FROM AUTHOR]

In this paper, we describe the Factored Value MCTS Hybrid Cost-Max-Plus algorithm, a collection of decision-making algorithms (centralized, decentralized, and hybrid) for a multi-agent system in a collaborative setting that considers action costs. Our proposed algorithm is made up of two steps. In the first step, each agent searches for the best individual actions with the lowest cost using the Monte Carlo Tree Search (MCTS) algorithm. Each agent's most promising activities are chosen and presented to the team. The Hybrid Cost Max-Plus method is utilized for joint action selection in the second step. The Hybrid Cost Max-Plus algorithm improves the well-known centralized and distributed Max-Plus algorithm by incorporating the cost of actions in agent interactions. The Max-Plus algorithm employed the Coordination Graph framework, which exploits agent dependencies to decompose the global payoff function as the sum of local terms. In terms of the number of agents and their interactions, the suggested Factored Value MCTS-Hybrid Cost-Max-Plus method is online, anytime, distributed, and scalable. Our contribution competes with state-of-the-art methodologies and algorithms by leveraging the locality of agent interactions for planning and acting utilizing MCTS and Max-Plus algorithms. [ABSTRACT FROM AUTHOR]

We present a multi-agent system where agents can cooperate to solve a system of dependent tasks, with agents having the capability to explore a solution space and make inferences, as well as query for information under a limited budget. Re-exploration of the solution space only happens when an older solution expires. We investigate the effects of task dependencies, increasing the speed of the agents, the complexity of the problem space, and the query budgets available to agents. Specifically, we evaluate trade-offs between the agent's speed and query budget. We observe that increasing the speed of a single agent improves the system performance to a certain point only and increasing the number of faster agents may not improve the system performance due to task dependencies. Favouring faster agents during budget allocation enhances the system performance, in line with the "Matthew effect". [ABSTRACT FROM AUTHOR]

Existing studies of the multi-group dynamics of prejudiced societies focus on the social-psychological knowledge behind the relevant processes. We instead create a multi-agent framework that simulates the propagation of prejudice and measures its tangible impact on prosperity. Levels of prosperity are tracked for individuals as well as larger social structures including groups and factions. We model social interactions using the Continuous Prisoner's Dilemma (CPD) and a new agent type called a prejudiced agent. Our simulations show that even modeling prejudice as an exclusively out-group phenomenon nonetheless generates implicit in-group promotion, leading to higher relative prosperity of a prejudiced population. This skew in prosperity is seen to be correlated to factors such as size difference between groups, and the fraction of prejudiced agents in a group. Our model is a step towards a deeper understanding of the origins, propagation, and ramifications of prejudice through simulative studies grounded in apt theoretical backgrounds. [ABSTRACT FROM AUTHOR]

Historically, the concept of consensus formation through iterative averaging was initially propounded by M. H. DeGroot. Subsequently, it has gained prominence across a diverse spectrum of scientific disciplines. To a certain degree, a Krause mean process serves as a comprehensive model for the dynamics of opinion exchange, wherein opinions are represented as vectors. In this study, we present a framework for opinion exchange dynamics by means of the Krause mean process that is generated by a cubic doubly stochastic matrix with weak influence. The primary objective is to establish a consensus in the multi-agent system. [ABSTRACT FROM AUTHOR]

Background. The study is aimed at adapting, applying and developing distributed artificial intelligence technologies for the issues of information and analytical support of situational centers at the regional level and efficiency enhancement of their operation when managing the security and resilience of critical infrastructures. Digitalization of situational control requires the development and implementation of adaptive self-organizing systems based on knowledge and with high autonomy, pro-activeness and interoperability. Materials and methods. The work is based on a comprehensive analysis and generalization of the well-known methodological approaches potential to the engineering of applied multi-agent systems, as well as modern practices for the synthesis of network-centric systems for situational control of distributed objects. The methods and tools for designing multi-agent systems are reviewed. Results and conclusions. A generic methodology for multi-agent systems engineering to support the management of critical infrastructures resilience, based on the combined use of the principles of analysis and synthesis of security systems for critical facilities, adaptive situational control systems and agent-based applications, has been developed. A distinctive feature of the methodology is the expansion of the functionality of known technological solutions in terms of the implementation of a simulation apparatus within the agents and a unit for joint activities coordination through «untying interactions» and self-organization of the agents into coalitions based on the model of gradient computational fields. This will improve the performance of systems for ensuring the critical infrastructures security and resilience used in situational centers by maintaining a high level of controllability, adaptability and intelligentization of these systems applying multi-agent technologies.

The rise of additive manufacturing (AM) has positioned lattice infilling as a pivotal strategy for creating lightweight, customized engineering components. This study presents a generative method that enables the conformal design and stiffness prediction of complex gradient strut-node lattice structures. A stress-driven Multi-Agent System (MAS) is introduced for the parametric optimization of lattice material distribution, incorporating geometric limitations, stress factors, and AM constraints. A beam element model simplifies the numerical analysis of the structure linear stiffness. By applying the Response Surface Method (RSM), a numerical model is established, not only conducting a quantitative analysis on the sensitivity of MAS design variables but predicting mechanical performance. This method is validated by designing a supporting component, demonstrating that the optimized lattice design can achieve a linear stiffness 1.4 times greater than that of conventional uniform lattice infills for the same mass. This research provides a comprehensive framework for the efficient design and analysis of irregular lattice structures at a macroscopic scale.

In order to solve the coordinated operation of voltage and frequency of microgrids and achieve effective distribution of output power, we propose the application of fractional-order nonlinear equations in coordination. This method designs a distributed impulse coordinated control strategy, to achieve the coordinated operation of the system. The distributed coordinated control structure and mathematical model are established, and the distributed two-level coordinated control strategy of the microgrid system is adopted. Aiming at the secondary control problem of microgrids, a distributed coordinated control protocol is designed. The results showed that after adding the distributed second-level coordination control of frequency at 3 s, the output active power of the four distributed power sources in the microgrid model is maintained to an evenly divided state after about 1 s. The output voltage and frequency utilizing the microgrid’s decentralized power supply quickly reach the ideal reference value, within the allowable error range, the output of the system can achieve coordinated control, and the active power can be distributed proportionally.

The utilization of simulation modelling in supply chain management has proven to be a valuable tool for logistics service providers, enabling them to optimize their operations and meet customer demands efficiently. AnyLogic, with its diverse capabilities in discrete event simulation, system dynamics, and agent-based simulation, stands out as a powerful software solution for tackling complex supply chain challenges. This paper significantly contributes by demonstrating the practical implementation of AnyLogic in the modelling and simulation of agro-food product distribution network, particularly vegetable oils from the Cevital group. Additionally, the study integrates an optimization experiment to ascertain the optimal number of vehicles required to meet a specific customer demand while ensuring a high level of fleet utilization. The obtained results are very encouraging and demonstrate the feasibility and effectiveness of our approach.

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 time-delays, 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.

This paper explores the group consensus problem of second-order multi-agent systems (MAS) under asynchronous denial-of-service (DoS) attacks. Asynchronous DoS attacks involve the interruption of certain communication links, allowing the MAS to be reimagined as a switching system with a persistent dwell time (PDT). The agents in each group can be divided into three types, which are inter-act agents, intra-act agents with zero in-degree, and other agents. Then, according to the properties of the different agents, suitable agents are pinned. By leveraging the concepts of switching topology and the PDT, a suitable event-triggered control protocol is designed, along with the establishment of conditions to ensure the group consensus of the MAS. Moreover, through the construction of topology-dependent Lyapunov functions, the achievement of group consensus within the MAS under asynchronous DoS attacks is demonstrated. Subsequently, a numerical example is presented to validate the effectiveness of the proposed results.

A multi-area power system requires coordination to enhance reliability and reduce operating costs. Economic dispatch in such systems is crucial because of the uncertainties associated with variable loads and the increasing penetration of renewable energy sources. This paper presents a hierarchical consensus algorithm designed to determine the economic dispatch in a multi-area power system, accounting for the uncertainties in load and renewable generation. The proposed algorithm, which utilizes distributed agents, operates across three levels. Level 1 coordinates all areas, while levels 2 and 3 form a leader–follower consensus algorithm for overall economic dispatch. Breadth-first search is employed to identify the leader agent within each area. To address the uncertainties in loads and renewable generation, Monte Carlo simulations are performed. The efficacy of the proposed method is validated using the IEEE 39-bus and 118-bus systems, as well as a realistic 1968-bus power system in Taiwan. The traditional equal lambda method is employed to verify that the proposed approach is suitable for multi-area power systems using distributed computation.

Rapid growth in a number of developing nations’ mobile telecommunications sectors presents network operators with difficulties such as poor service quality and congestion, mostly because these locations lack a dependable and reasonably priced electrical source. In order to provide a sustainable and reasonably priced energy alternative for the developing world, this study provides a detailed examination of the core ideas behind renewable energy technology (RET). A multi-agent-based small-scaled smart base transceiver station (BTS) site reinforcement strategy is presented to manage energy resources by boosting resilience so to supply power to essential loads in peak demand periods by leveraging demand-side management (DSM). Diverse energy sources are combined to create interconnected BTS sites, which enable energy sharing to balance fluctuations by establishing a market that promotes economical energy. A MATLAB simulation model was developed to assess the effectiveness of the proposed system by using real load data and fast electric vehicle charging loads from five different base transceiver stations (BTSs) located throughout Pakistan’s southern area. In this proposed study, the base transceiver station (BTS) sites can share their energy through a multi-agent-based system. From the results, it is observed that, after optimization, the base transceiver station (BTS) sites trade their energy with the grid at rate of 0.08 USD/kWh and with other sites at a rate of 0.04 USD/kWh. Therefore, grid dependency is decreased by 44.3% and carbon emissions are reduced by 71.4% after the optimization of the base transceiver station (BTS) sites.

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.

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.