Multi-agent Simulation System Research Articles

Application of geospatial multi-agent system for simulation of different aspects of tuberculosis transmission

BACKGROUND. The paper presents epidemiological process modeling, with a focus on tuberculosis utilizing multi-agent system. MATERIALS AND METHODS. This study involves the development of an algorithm that uses the potential of artificial intelligence to create a geospatial model that highlights the different routes of tuberculosis transmission. The simulation process itself is characterized by a number of key stages, including initialization of the city, calibration of health parameters, simulation of the working day, spread of infection, evolution of disease trajectories, rigorous statistical calculations and transition to the next day. A comprehensive description of the course of active tuberculosis according to official data of the World Health Organization is given. Each agent is provided with an appropriate and consistent model that includes relevant health attributes and necessary rules for their dynamic evolution. CONCLUSIONS. The model’s results exhibit stability and lack of significant fluctuations. The statistical values obtained for infected, latent, and recovered individuals align well with known medical data, confirming the model’s adequacy. The proposed model allows for tracking and analyzing the life and behavior of each individual agent, enabling a thorough assessment of tuberculosis infection spread and the development of prevention strategies.

Application of geospatial multi-agent system for simulation of different aspects of tuberculosis transmission

BACKGROUND. The article presents the modeling of the epidemiological picture of tuberculosis (TB) using a multi-agent system. This is to provide an in-depth multi-agent modeling study with comprehensive strategies related to TB treatment and prevention.
 OBJECTIVE. To develop an algorithm that uses the capabilities of artificial intelligence and neural networks to build a geospatial model of TB transmission.
 MATERIALS AND METHODS. This study involves the development of an algorithm that uses the potential of artificial intelligence to create a geospatial model that highlights the different routes of TB transmission. The simulation process itself is characterized by a number of key stages, including initialization of the city, calibration of health parameters, simulation of the working day, spread of infection, evolution of disease trajectories, rigorous statistical calculations and transition to the next day. A comprehensive description of the course of active TB according to official data of the World Health Organization is given. Each agent is provided with an appropriate and consistent model that includes relevant health attributes and necessary rules for their dynamic evolution.
 RESULTS AND DISCUSSION. The process of modeling the transmission of TB in the urban landscape has been thoroughly investigated. Attributes and functional dependencies are divided into three different groups: static (or deterministic), stochastic, and empirical. Empirical parameters that determine the contagiousness of TB patients are subjected to careful analysis, during which the dynamic nature of their actual contagiousness is carefully compared. Therefore, the empirical parameters that determine infectivity are carefully established, which additionally allows for the quantitative determination of the level of TB infection. A complex simulation is presented that illustrates the spread of TB in a completely healthy environment devoid of any preventive or therapeutic measures. This simulation serves as a crucial study of the spread of the disease. To test the adequacy of the model and its sensitivity to the main parameters that determine the course of TB, a series of experiments using three different approximations, namely: a basic model, a model that includes mortality factors and a comprehensive model covering all relevant aspects, was conducted. Statistics used in this context include the number of infected and latent persons, recoveries and deaths. The article presents a comparative analysis between statistical information and a model that offers a comprehensive picture of the state of health of each person. The model allows careful monitoring of various factors, such as interpersonal contacts, place of residence, workplace, modes of transport used to move from place to place, and other vital aspects of a person’s life, together contributing to a more complete understanding of the dynamics of the disease.
 CONCLUSIONS. The calculations show the stability of the results and the absence of large fluctuations. The obtained statistical values of infected, latent and recovered people correlate with known medical data, and therefore, confirm the adequacy of the proposed model. This model allows tracking and analyzing the life and behavior of each agent, which allows for a detailed assessment and analysis of the spread of TB, as well as to develop a strategy to prevent the spread of this infection.

Design and Implementation of a Novel Compact Laboratory for Web-Based Multiagent System Simulation and Experimentation

Design and Implementation of a Novel Compact Laboratory for Web-Based Multiagent System Simulation and Experimentation

An executable domain-specific modeling language for simulating organizational auction-based coordination strategies for crisis response

Emergency response environments in which the crisis occurs include many homogenous and heterogeneous entities, e.g., organizations and persons. These entities need to collaborate to address a crisis. However, most of the time, the organizations that are involved in a crisis suffer from communication and coordination problems. This may cause unfair resource distribution. To mitigate the problem, there is a pressing need to construct models of emergency response environments and simulate the coordination strategies proactively, before the onset of any crisis. This paper highlights the complexity of the system formed by interacting organizations during a crisis and the difficulty in programming for the simulation of such complex systems. To cope with this issue, this paper presents an executable domain-specific modeling language called CoorERE. The language facilitates modeling emergency response environments for the simulation of one of the most common coordination strategies, i.e., auction-based coordination strategies in crisis response. CoorERE is used for the graphical modeling of emergency response environments and simulation of both inter and intra-organizational auction-based coordination strategies. The paper presents two case studies with different complexities to show the applicability of CoorERE. An empirical study is conducted to compare CoorERE with MDD4ABMS, which is one of the most recent tools for modeling and simulation of multi-agent systems. The evaluation includes both objective and subjective assessments. The objective assessment considers the development effort required for modeling a complete case study, while the subjective assessment compares the tools in terms of satisfying modeling method requirements and quality metrics such as usability, productivity, and expressiveness. The results of the evaluation indicate that CoorERE reduces the development effort by about 47%. In terms of quality metrics, CoorERE obtained higher scores than MDD4ABMS for all the measurements considered, indicating its superior usability, productivity, and expressiveness.

Using a Soccer team as a test bed for multi-agent systems simulation

The STMAS system is intended to imitate a soccer team and its behavior; we believe it can be used effectively as a test bed for multi-agent systems. It is constructed utilizing distributed agents that interact, communicate, and negotiate with each other to achieve the team objectives. It is based on the Jade simulation platform. The system is tested and compared to a pure soccer team using multiple MAS techniques. The results demonstrated that applying MAS techniques of negotiation and task distribution improves team performance, and STMAS is offered as an efficient test bed for new and distinct MAS techniques with varied scenario experiments. In addition, a mathematical model is created to compare the simulation results. Overall, STMAS provides a versatile and efficient MAS simulation and evaluation test bed. It is an excellent platform for comparing and evaluating various MAS approaches.

Flexible simulation of traffic with microservices, agents & REST

Hypermedia Multi-Agent System (MAS) Simulation is a novel approach to building agent-based simulations in which the environment is modelled as a set of linked hypermedia resources that are implemented using microservices. This paper discusses the implementation of an prototypical simulation system based on this concept and the lessons learnt in the process.

A multi-agent system simulation based approach for collision avoidance in integrated Job-Shop Scheduling Problem with transportation tasks

At the operational level of Flexible Manufacturing Systems (FMS), several research works have developed solutions not fully realizable considering the operational constraints, which can be difficult to model. One of the well-known problems in FMS is the Job-Shop Scheduling Problem (JSSP) with transportation tasks. Indeed, this problem is addressed by many researchers, but most of their studies do not consider operational constraints such as collision avoidance between transporters. Moreover, optimized scheduling may contain deadlock situations due to often neglected geometric constraints. Therefore, in this paper, we propose a simulation approach based on a multi-agent system to test the optimized results known in the literature in more realistic conditions, where collision avoidance between transporters is considered. We propose as well a sim-optimization approach to explore suitable solutions considering collision avoidance constraints. In addition, an algorithm is proposed to solve deadlocks. The obtained results highlight the impact of collision avoidance on the performance of the literature solutions while being more realistic. Furthermore, these results demonstrate the efficiency of the proposed algorithms, since all identified collisions are avoided and all deadlocks are also solved. The proposed approach is expected to be more attractive to the industries as it ensures a small gap between the theoretical and actual results.

Electric bus charging facility planning with uncertainties: Model formulation and algorithm design

This paper investigates the electric bus charging facility planning (EB-CFP) problem for a bus transit company operating a heterogeneous electric bus (EB) fleet to provide public transportation services, taking into account uncertainties in both EB travel time and battery degradation. The goal of the EB-CFP problem is to determine the number and type of EB chargers that should be deployed at bus terminals and depots to meet daily EB charging demand while minimizing total cost. The problem is formulated as a two-stage stochastic programming model, with the first stage determining the EB charger deployment scheme and the second stage estimating the EB daily operational cost with respect to a predetermined EB trip timetable for a given EB charger deployment scheme. To effectively address the second stage problem, a multi-agent EB transit simulation system that mimics the daily EB operation process is developed. We then design two heuristic methods, the reinforcement learning (RL)-based method and the surrogate-based optimization (SBO), that use the developed multi-agent EB transit simulation system to solve the two-stage stochastic programming model for large-scale instances. Lastly, we run a series of experiments on a fictitious EB transit network and a real-world EB transit network in Singapore to evaluate the performance of the models and algorithms. In order to improve the performance of the EB transit system, some managerial insights are also provided to urban bus transit companies.

Assessing the effectiveness of intervention policies for reclaimed water reuse in China considering multi-scenario simulations

The reclaimed water is an effective tool for water environmental management. And in China, government intervention is regarded as a powerful instrument to realize its large-scale utilization due to the public welfare and social characteristics. Therefore, the tripartite evolutionary game and multi-agent simulation system were combined to explore how intervention policies, such as price subsidies and regulation of water information disclosure, can influence the behaviors of stakeholders involved in the reclaimed water market, so as to alleviate water environment crisis. The results showed that: (1) The incentive of price subsidy shows noticeable regional differences due to different initial reclaimed water utilization rates, and the policy effect on regions with medium level utilization (25%–40%) > regions with high utilization (higher than 40%) > regions with low utilization (lower than 25%); (2) enhancing water information supervision can positively regulate the stimulation of subsidy policy in regions with low wastewater reuse; (3) the intensity of price subsidies has an inverted U-shaped relationship with the market stability in regions with high wastewater reuse; (4) diverse regulatory models on water information disclosure show various chain effects on improving wastewater reuse.

Mitigating Catastrophic Forgetting with Complementary Layered Learning

Catastrophic forgetting is a stability–plasticity imbalance that causes a machine learner to lose previously gained knowledge that is critical for performing a task. The imbalance occurs in transfer learning, negatively affecting the learner’s performance, particularly in neural networks and layered learning. This work proposes a complementary learning technique that introduces long- and short-term memory to layered learning to reduce the negative effects of catastrophic forgetting. In particular, this work proposes the dual memory system in the non-neural network approaches of evolutionary computation and Q-learning instances of layered learning because these techniques are used to develop decision-making capabilities for physical robots. Experiments evaluate the new learning augmentation in a multi-agent system simulation, where autonomous unmanned aerial vehicles learn to collaborate and maneuver to survey an area effectively. Through these direct-policy and value-based learning experiments, the proposed complementary layered learning is demonstrated to significantly improve task performance over standard layered learning, successfully balancing stability and plasticity.

Exploring the impact of set-based concurrent engineering through multi-agent system simulation

Abstract Set-based concurrent engineering (SBCE), a process that develops sets of many design candidates for each subproblem throughout a design project, proposes several benefits compared to point-based processes, where only one design candidate for each subproblem is chosen for further development. These benefits include reduced rework, improved design quality, and retention of knowledge to use in future projects. Previous studies that introduce SBCE in practice achieved success and had very positive future outlooks, but SBCE encounters opposition because its core procedures appear wasteful as designers must divide their time among many designs throughout the process, most of which are ultimately not used. The impacts of these procedures can be explored in detail through open-source computational tools, but currently few exist to do this. This work introduces the Point/Set-Organized Research Teams (PSORT) modeling platform to simulate and analyze a set-based design process. The approach is used to verify statements made about SBCE and investigate its effects on project quality. Such an SBCE platform enables process exploration without needing to commit many projects and resources to any given design.

Multi-agent-based collaborative regulation optimization for microgrid economic dispatch under a time-based price mechanism

• This paper provides a multi-agent based coordinated dispatch strategy for the economic dispatch of the microgrid under a time-based price mechanism. • We develop a microgrid optimization model for the microgrid operation process, which includes battery regulation and user satisfaction. • The established optimization model is solved using a MACPSO algorithm, and the agent communication mechanism in the microgrid is examined. • The performance comparison using different algorithms (PSO, IPSO, MAPSO and MACPSO) has been presented. • The proposed multi-agent coordinated dispatch strategy is validated by designing a multi-agent system simulation environment and using the Java agent development (JADE) framework. The economic optimal dispatch of a microgrid is a challenging task with significant economic and social implications. Under a time-based price mechanism, this paper proposes a multi-agent-based coordinated dispatch strategy for the microgrid's economic dispatch. The information between the agents in the microgrid can be fed back in time to reduce microgrid operation failures by coordinated control between the demand-side load, the battery, and the power generation side micro-sources. The output of other micro-sources is then coordinated using an appropriate battery charging and discharging plan based on the characteristics of the time-based electricity price mechanism. Following that, an economical microgrid operation model is established and solved using a multi-agent chaotic particle swarm optimization (MACPSO) algorithm, which considers user satisfaction. Finally, a multi-agent system (MAS) simulation environment is built using the Java agent development (JADE) framework. The proposed microgrid with multi-agent coordinated control can effectively improve its operation efficiency, according to a comparative analysis of examples.

Structural Optimization of Architectural Environmental Art Design Based on Multiagent Simulation System

Underthe background of the new era, the architectural environment design is constantly changing. Architectural environment design is to beautify the environment and provide people with a pleasant architectural environment. No matter what time, beauty is the eternal theme and the pursuit of people. Structural bionic design is based on the natural prototype, which is transformed into understandable digital or technical models by extracting relevant structural and behavioral features as design elements. The application of this design technique in architectural devices not only has strong structural logic but also artistic beauty, and can form novel spatial form and structural form of architectural devices. By analyzing the present situation and existing problems of theoretical research and simulation experiment of multiagent system, the necessity of designing a multiagent system planning algorithm and developing distributed real-time simulation experiment system is pointed out. A good environmental art design scheme can be designed in many different styles, and the performance of the design renderings will also appear in different pictures. The really chosen design scheme depends on the designer’s conception theme and then look at the performance of the renderings. In recent years, the emergence of multiagent has had a great impact on building environment design. Multiagent technology is a comprehensive information technology that integrates artificial intelligence, network, digital, and other technologies.

Impact Estimation of Unplanned Urban Rail Disruptions on Public Transport Passengers: A Multi-Agent Based Simulation Approach.

Once unplanned urban rail disruptions occur, it is essential to evaluate the impacts on public transport passengers since impact estimation results enable transit agencies to verify whether alternative transit services have adequate capacity to evacuate the affected rail passengers and to adopt effective emergency measures in response to the disruptions. This paper focuses on estimating the impacts of unplanned rail line segment disruptions on rail passengers as well as original bus passengers, as the latter are overlooked in existing studies. A method of identifying affected rail passengers based on passenger tap-in time is proposed, which is helpful for evaluating the scale and origin-destination distribution of the affected passengers. Passengers’ response behaviors are analyzed and modeled in a multi-agent simulation system. The system realizes the simulation of the multimodal evacuation process, in which a rule-based logit model is employed to describe passengers’ travel selection behavior and the Monte Carlo method is utilized to address the issue of uncertainty in passengers’ travel selection. In particular, the original bus passengers are integrated into the simulation and interact with rail passengers. Finally, some indicators assessing the impacts on rail passengers and bus passengers are presented, and a case study based on the Ningbo urban rail transit network is conducted.

An evacuation simulation model of pedestrian flow using Bayesian Nash equilibrium and a Multi-Agent System

Abstract. The shortage of experimental data for individual behaviours has hampered systematic research on pedestrian behaviours and the refined development of evidence informed rules for pedestrian movement in simulation models. This research proposes an evacuation simulation model of pedestrian flow based on Bayesian Nash Equilibrium (BNE) and Multi-Agent System (MAS). In this paper, BNE was used to augment the logics of pedestrian decision-making processes in an evacuation MAS simulation and to improve evacuating agents’ movement and behaviours. A detailed introduction of the construction process and implementation details for the initial model as well as the visualization of experiment results are provided in this paper. Limitations and several potential future research directions are also identified.

Construction of the Simulation Algorithm for Evacuation of Commercial Complexes under the Panic Factor

In order to study the behavior and characteristics of crowd evacuation in buildings, and then organize crowd evacuation reasonably and efficiently in emergency, a simulation model of crowd evacuation in buildings is proposed, which not only accords with reality, but also comprehensively considers many factors. Aiming at the shortcomings of the crowd evacuation simulation model in the existing building emergency evacuation, this model provides a multi-agent evacuation simulation method and system based on panic emotion, which aims to more truly simulate the crowd evacuation process in emergency, and has important practical significance for the crowd evacuation plan in emergency and how to effectively organize crowd evacuation and reduce casualties in emergencies. In order to achieve the above goal, a multi-agent evacuation simulation method based on leaders and panic emotions is proposed. The method includes the following three stages: perception stage, decision-making stage and action stage, and each stage is explained step by step.

A Socio-Inspired Methodology and Model for Advanced and Opportunistic Interactions between Industrial IoT Objects

The concept of the Internet of Things (IoT) is widely discussed. IoT is one of the emerging technologies that have caught the attention of many researchers. The increase in the number of exchanges of services between heterogeneous or homogeneous connected objects with the integration of social networking concepts gives rise to the concept of the Social Internet of Things (SIoT). The SIoT concept contributes to the evolution of interactions between industrial objects by improving deterministic mechanisms towards intelligent interactions. The integration of the SIoT concept into the Industrial Internet of Things (IIoT) gives rise to the Social Internet of Industrial Things (SIoIT) and plays an important role in improving system performance in Industry 4.0. In this article, we propose an innovative methodology and a model of socio-inspired interaction between industrial communicating objects inspired by sociological approaches. Thanks to this model, socialized industrial communicating objects form a community of objects, autonomously and dynamically, by exchanging messages to know each other perfectly, and service requests between objects are executed adaptively according to the principles of social interaction governed by socio-inspired strategies and conditions. The model is implemented, tested and validated in a Netlogo multi-agent system simulation environment.

A Design of Multi-Agent System Simulation Platform Based on Unmanned Ground Vehicles and A Research on Formation Control Protocol

A Design of Multi-Agent System Simulation Platform Based on Unmanned Ground Vehicles and A Research on Formation Control Protocol

Multi-Agent Surveillance System Of Fake News Spreading In Scale-Free Networks

The modern world is based on information. With the current development of communication technologies, messages are spreading rapidly through social networks, gathering vast numbers of users. Along with facilitating the transfer of information on a large scale, more remarkable ability to apply and influence life, politics and money have so-called “fake news”, understood as artificially crafted false information to achieve specific goals. The growing influence of such fake news increases interest in social scientists and generates the need to develop measures to reduce this phenomenon. Implementing countermeasures on a large scale is costly, and the results are often uncertain. Therefore, models and systems are being developed to predict the dynamics and course of the spread of “fake news” to study the influence of various factors on this very process. This article presents a multi-agent simulation system for spreading fake information in the so-called scale-free networks.

Socialization of Smart Communicative Objects in Industrial Internet of Things

In the recent past the Internet of Things (IoT) is moving from interactive objects to smart objects. The number of smart devices or sensors built-in becomes larger. The increase in the number of services exchanged between connected objects has led to the creation of a new concept called Social IoT (SIoT), in which heterogeneous or homogeneous objects can interact and request or exchange services, which makes it possible to include social or human actions in the IoT platform. The use of technologies based on the concept of the Internet of Things in applications and industrial sectors has built a new concept of research called the Industrial Internet of Things (IIoT). The integration of the SIoT concept into the Industrial Internet of Things (IIoT) plays an important role in improving system performance in Industry 4.0 and gives rise to the Social Internet of Industrial Things (SIoIT). In this context, we have revealed a new socio-inspired interaction model between industrial communicating objects inspired by sociological approaches that transform objects into socialized industrial communicating objects. These objects form a community, autonomously and dynamically, by exchanging messages to know each other perfectly, and service requests between objects are realized in an adaptive way according to the principles of social interaction governed by socio-inspired strategies and conditions. The model is evaluated using Netlogo multi-agent system simulation environment.