Multi-agent Simulation Platform Research Articles

Comparison of execution times of three multi-agent platforms (Netlogo/GAMA/Repast simphony) based on the LASCAR model

ABSTRACT Three multi-agent simulation platforms (NetLogo, GAMA, Repast Simphony) were tested to find the best platform for the LASCAR model (LAndscape StruCture And Runoff) execution. The LASCAR model simulates the interactions between surface runoff and objects constituting agricultural landscapes (hedges, roads, cultivated plots, etc.). The objective was to evaluate the execution times of NetLogo in view of a potential platform migration of the LASCAR model. To this end, a comparison of the platforms, which is a process that is still rare in the literature, was conducted. We focused on the model execution times during the first 200 iterations and for two simulation environments of different sizes (17.6 km2 and 1.52 km2). The results show that NetLogo is, respectively, 2.7 times and 1.4 times faster than GAMA and Repast Simphony in a simulation environment with 45,000 active cells.

Multi-Agent Dynamic Area Coverage Based on Reinforcement Learning with Connected Agents

Dynamic area coverage with small unmanned aerial vehicle (UAV) systems is one of the major research topics due to limited payloads and the difficulty of decentralized decision-making process. Collaborative behavior of a group of UAVs in an unknown environment is another hard problem to be solved. In this paper, we propose a method for decentralized execution of multi-UAVs for dynamic area coverage problems. The proposed decentralized decision-making dynamic area coverage (DDMDAC) method utilizes reinforcement learning (RL) where each UAV is represented by an intelligent agent that learns policies to create collaborative behaviors in partially observable environment. Intelligent agents increase their global observations by gathering information about the environment by connecting with other agents. The connectivity provides a consensus for the decision-making process, while each agent takes decisions. At each step, agents acquire all reachable agents’ states, determine the optimum location for maximal area coverage and receive reward using the covered rate on the target area, respectively. The method was tested in a multi-agent actor-critic simulation platform. In the study, it has been considered that each UAV has a certain communication distance as in real applications. The results show that UAVs with limited communication distance can act jointly in the target area and can successfully cover the area without guidance from the central command unit.

Multi-Vehicle Interactive Lane-Changing Velocity Change Model Based on Potential Energy Field

To quantitatively analyze velocity change in the interaction of multiple vehicles during the lane-changing process, this study defines the concepts of driving constraint region and multi-vehicle interaction region (MVIR) by analyzing the potential energy field. The attraction and repulsion effects of the target vehicle and surrounding vehicles are attributed to the area change in the overlapping parts of their MVIRs. A multi-vehicle interactive lane-changing method considering MVIR is proposed. A velocity change model of multi-vehicle interactive lane-changing is established to reveal the change regularity of velocity under multi-vehicle interactive lane-changing. The model parameters are calibrated using a P3-DT Beidou high-precision positioning direction finding receiver to collect vehicle coordinates and velocity. The error of vehicle velocity variation was less than 11%, which verified the validity of the velocity change model. The research results can guide drivers in completing the lane-changing process safely and quickly, and provide theoretical support for micro-traffic flow simulation, road traffic safety proactive prevention and control, multi-vehicle lane-changing rules, and multi-agent simulation platform construction.

A collaboration of multi-agent model using an interactive interface

Multi-agent reinforcement learning algorithms scarcely attend to noisy environments, in which agents are inhibited from achieving optimal policy training and making correct decisions. This work investigates the effect of noises in multi-agent environments and proposes a multi-agent actor-critic with collaboration (MACC) model. The model uses lightweight communication to overcome the interference from noises. There are two policies for each agent in MACC: collaboration policy and behavior policy. The behavior of an agent not only depends on its own state but also be influenced by each other agent through a scalar, collaboration value. The collaboration value is generated by the collaboration policy for each individual agent, and it ensures a succinct consensus about the environment. This paper elaborates on the training of the collaboration policy and specifies how it coordinates the behavior policy in a manner of temporal abstraction mechanism, while the observation sequence is considered for more accurate perception. Several experiments on multi-agent collaboration simulation platforms demonstrate that the MACC performs better than baselines in noisy environments, especially in partially observable environments.

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

A Design of Multi-Agent System Simulation Platform for UGV and Research on its Formation Control Protocol

A Design of Multi-Agent System Simulation Platform for UGV and Research on its Formation Control Protocol

Potential Benefits of Increased Public Transit Ridership in Medium Sized Cities: A Case Study

At the beginning of the twentieth century, the United States was leading in the public transit sector, but following World War II, private automobiles became more affordable and gained popularity. Transportation infrastructure investments that increased road capacity further facilitated the increase in automobile use at the expense of reduced public transit ridership. With the increase of dependency on automobiles and the continuing growth of private automobile ownership and use, various problems became major challenges in big cities of USA. These include traffic congestion, air pollution, road and parking infrastructure costs, energy consumption, traffic safety, fewer mobility options for the non-drivers, and a decline in the image and use of public transit. This study uses a medium sized city, Birmingham as a case study to investigate the potential of public transit to reduce automobile trips and in turn improve the overall performance of the road network by addressing the abovementioned challenges. An agent-based simulation model was developed for the Birmingham metropolitan region using the Multi-agent Transport Simulation (MATSim) platform. Three scenarios were considered with gradually increased transit ridership to identify the benefits of increased public transit. Traffic volume, network average speed, and travel times were used as performance measures for the evaluation of the designated scenarios. Results suggest that modal shifts toward public transit and reduction in travel demand for an automobile can result in improvements in speed and travel time for all users. Therefore, investments for improving transit quality and frequency of service, as well as campaigns to improve the image of public transit and make it a mode of choice for transportation users can increase transit ridership and, in turn, improve network operations, thus are deemed worthy for medium sized cities.

HGHA: task allocation and path planning for warehouse agents

PurposeWith the rapid development of e-commerce, logistics demand is increasing day by day. The modern warehousing with a multi-agent system as the core comes into being. This paper aims to study the task allocation and path-planning (TAPP) problem as required by the multi-agent warehouse system.Design/methodology/approachThe TAPP problem targets to minimize the makespan by allocating tasks to the agents and planning collision-free paths for the agents. This paper presents the Hierarchical Genetic Highways Algorithm (HGHA), a hierarchical algorithm combining optimization and multi-agent path-finding (MAPF). The top-level is the genetic algorithm (GA), allocating tasks to agents in an optimized way. The lower level is the so-called highways local repair (HLR) process, avoiding the collisions by local repairment if and only if conflicts arise.FindingsExperiments demonstrate that HGHA performs faster and more efficient for the warehouse scenario than max multi-flow. This paper also applies HGHA to TAPP instances with a hundred agents and a thousand storage locations in a customized warehouse simulation platform with MultiBots.Originality/valueThis paper formulates the multi-agent warehousing distribution problem, TAPP. The HGHA based on hierarchical architecture solves the TAPP accurately and quickly. Verifying the HGHA by the large-scale multi-agent simulation platform MultiBots.

Influence of exits and evacuees on evacuation efficiency

In order to study the evacuation process of crowd in evacuation space containing four exits (two exits on one side), a simulation environment was built by using multi-agent simulation platform NetLogo, and the evacuation simulation model was constructed. The influence of exit location, exit width and evacuee number on the evacuation efficiency was studied, the results show that: with the exit moving to the middle, the evacuation time first decreases and then increases, and the decreasing rate of evacuation time decreases gradually and the increasing rate increases gradually; when the evacuee number is small, the evacuation time decreases with the increase of exit width, until the evacuation time remains unchanged when the exit width is large enough. However, when the evacuee number is large, the evacuation time in different positions decreases with the increase of exit width, and the overall decrease rate decreases; with the exit moving to the middle, the increasing rate of evacuation time with the increase of evacuee number at different exit widths decreases at first and then increases. The research results can provide some reference value for the evacuation exit design of building and emergency command of crowd evacuation.

Demand responsive transport: Generation of activity patterns from mobile phone network data to support the operation of new mobility services

Abstract Demand Responsive Transport (DRT), covering the first/last mile of a journey, plays a pivotal role in the delivery of a seamless integrated door-to-door service, which is a fundamental requirement for the implementation of Mobility as a Service (MaaS). Business models currently in use do not deliver sustainable and durable DRT in urban areas. This can be minimised using transport modelling tools ahead of the operation phase. However, transport models are not fit for purpose when it comes to model on-demand shared mobility services and the integration of these services in a complex public transport ecosystem. This paper focuses on how to model demand for ride-shared mobility services and how to plan for these services when running in integration with mass transit. An Agent Based Model (ABM), built in the open-source Multi-Agent Transport Simulation (MatSim) platform for Bristol (UK), has used an activity-based approach to model demand for two New Mobility Services (NMS). This was then generated using anonymised and aggregated Mobile phone Network Dataset (MND), both as a trip-based and trip chains dataset to assess the capabilities of MND. Results show that the simulations built using the trip chains MND datasets (722,752 agents generated) lead to better insights in users’ travel patterns. An advanced method using additional data sources covering land-use (location of business, services and transport facilities) was used to infer purpose and mode of transport during the multimodal journeys. The output of the ABM predicts demand for two flexible on-demand services, identifying best routes to maximise the number of users served and quantifying the benefits in the integration with public transport services and in modal shift from private cars. This is expected to be useful either for Local Authorities for transport planning purposes, and for operators looking at financially sustainable DRT.

Decision Support for Small Players Negotiations Under a Transactive Energy Framework

This paper proposes a decision support model to optimize small players’ negotiations in multiple alternative/complementary market opportunities. The proposed model endows players with the ability to maximize their gains in electricity market negotiations. The proposed approach is integrated in a multi-agent simulation platform, which enables experimenting different market configurations, thus facilitating the assessment of the impact of negotiation outcomes in distinct electricity markets. The proposed model is directed to supporting the actions of small players in a transactive energy environment. Therefore, the experimental findings include negotiations in local markets, negotiations through bilateral contracts, and the participation in wholesale markets (through aggregators). The validation is performed using real data from the Iberian market, and results show that by planning market actions considering the expected prices in different market opportunities, small players are able to improve their benefits from market negotiations.

Games Ready to Use: A Serious Game for Teaching Natural Risk Management

Background.Risk management, and in particular the management of natural hazards and territorial risks has become an essential skill for civil engineers. Teaching risk management to engineering students is therefore crucial, but is also challenging: it looks too abstract to students, and practical works are complex and expensive to organise. It also involves interconnected mechanisms coupling human and technical aspects, that are difficult to explain. Aim. The challenge is then to propose a serious game able to support the teaching of territorial risk management to engineering students. As part of their curriculum, these students are expected to learn various concepts and notions: territorial risk, vulnerability of a territory, resilience, risk perception, multi-criteria analysis and balanced management. Method. In order to support risk management teaching, we propose SPRITE, an agent-based serious game using a concrete case study which is exemplary in terms of risk management: the coastal floods on the Oleron Island (France). SPRITE places the player (the student) in the role of a local councillor of the Oleron Island, who must ensure the safety and well-being of the island residents, while maximising performance with respect to economic and environmental issues, in a context of coastal flood risk. Results. The model is fully implemented in GAMA, an open-source multi-agent geographical simulation platform, and the game is already playable. It was used at the University of Bordeaux in a course on risk management dedicated to students in the Master of Geology and Civil Engineering. The evaluation of engagement and motivation with the game and learning from playing is very positive. Conclusions. The results from the game evaluation are encouraging. Short term future work will mainly be dedicated to pursuing this evaluation, and comparing results between students using SPRITE vs students following a more traditional course. Longer term prospects include several improvements of the model and the interface and implemented multiplayer features.

Percolation phenomenon in connected vehicle network through a multi-agent approach: Mobility benefits and market penetration

This paper presents an integrated multi-agent approach, coupled with percolation theory and network science, to measure the mobility impacts (i.e., mean travel time of the system) of connected vehicle (CVtio) network at varying levels of market penetration rate. We capture the characteristics of a CV network, i.e., node degree distribution, vehicular clustering, and giant component size to verify the existence of percolation phenomenon, and further connect the emergence of mobility benefits to the percolation phase transition in the CV network. We show the percolation phase transition properties to appear in a dynamic CV network with time-correlated link and node dynamics. An analytical framework was developed to evaluate the CV network attributes with varying market penetrations (MP) and connection ranges (CR) to identify percolation phenomenon in a mixed CV and Non-CV environment. In addition, a multi-agent CV simulation platform was created to further measure (1) how varying MPs and CRs affect the network-wide mobility measured by the mean travel time of the network; and (2) when percolation transition occurs in CV network to capture the critical MP and CR. Percolation phenomenon in CV network was further validated with the analytical assessments. The results show that (1) percolation phase transition phenomenon is a function of both market penetration and communication range; (2) percolation phase transitions in both mobility and CV network are highly correlated; (3) the application can reduce the average travel time of the system by up to 20% with reasonable market penetration and communication range; (4) critical market penetration is sensitive to communication range, and vice versa; (5) at least 70% of the CVs on the network are required to show in the same cluster for mobility benefits to appear; and (6) for high levels of MP or CR, a low probability of connectivity (PC) does not dramatically change the mean travel time. These results provide solid supports to create evidence-driven frameworks to guide future CV deployment and CV network analysis.

Simulation of electric vehicle driver behaviour in road transport and electric power networks

The integration of electric vehicles (EVs) will affect both electricity and transport systems and research is needed on finding possible ways to make a smooth transition to the electrification of the road transport. To fully understand the EV integration consequences, the behaviour of the EV drivers and its impact on these two systems should be studied. This paper describes an integrated simulation-based approach, modelling the EV and its interactions in both road transport and electric power systems. The main components of both systems have been considered, and the EV driver behaviour was modelled using a multi-agent simulation platform. Considering a fleet of 1000EV agents, two behavioural profiles were studied (Unaware/Aware) to model EV driver behaviour. The two behavioural profiles represent the EV driver in different stages of EV adoption starting with Unaware EV drivers when the public acceptance of EVs is limited, and developing to Aware EV drivers as the electrification of road transport is promoted in an overall context. The EV agents were modelled to follow a realistic activity-based trip pattern, and the impact of EV driver behaviour was simulated on a road transport and electricity grid. It was found that the EV agents’ behaviour has direct and indirect impact on both the road transport network and the electricity grid, affecting the traffic of the roads, the stress of the distribution network and the utilization of the charging infrastructure.

Traffic Control Models Based on Cellular Automata for At-Grade Intersections in Autonomous Vehicle Environment

Autonomous vehicle is able to facilitate road safety and traffic efficiency and has become a promising trend of future development. With a focus on highways, existing literatures studied the feasibility of autonomous vehicle in continuous traffic flows and the controllability of cooperative driving. However, rare efforts have been made to investigate the traffic control strategies in autonomous vehicle environment on urban roads, especially in urban intersections. In autonomous vehicle environment, it is possible to achieve cooperative driving with V2V and V2I wireless communication. Without signal control, conflicted traffic flows could pass intersections through mutual cooperative, which is a remarkable improvement to existing traffic control methods. This paper established a cellular automata model with greedy algorithm for the traffic control of intersections in autonomous vehicle environment, with autonomous vehicle platoon as the optimization object. NetLogo multiagent simulation platform model was employed to simulate the proposed model. The simulation results are compared with the traffic control programs in conventional Synchro optimization. The findings suggest that, on the premises of ensuring traffic safety, the control strategy of the proposed model significantly reduces average delays and number of stops as well as increasing traffic capacity.

A wolf pack hunting strategy based virtual tribes control for automatic generation control of smart grid

This paper proposes a novel electric power autonomy to satisfy the requirement of power generation optimization of smart grid and decentralized energy management system. A decentralized virtual tribes control (VTC) is developed which can effectively coordinate the regional dispatch centre and the distributed energy. Then a wolf pack hunting (WPH) strategy based VTC (WPH-VTC) is designed through combining the multi-agent system stochastic game and multi-agent system collaborative consensus, which is called the multi-agent system stochastic consensus game, to achieve the coordination and optimization of the decentralized VTC, such that different types of renewable energy can be effectively integrated into the electric power autonomy. The proposed scheme is implemented on a flexible and dynamic multi-agent stochastic game-based VTC simulation platform, which control performance is evaluated on a typical two-area load–frequency control power system and a practical Guangdong power grid model in southern China. Simulation results verify that it can improve the closed-loop system performances, increase the utilization rate of the renewable energy, reduce the carbon emissions, and achieve a fast convergence rate with significant robustness compared with those of existing schemes.

Multi-agent based modeling for electric vehicle integration in a distribution network operation

The purpose of this paper is to present a multi-agent based modeling technology for simulating and operating a hierarchical energy management of a power distribution system with focus on EVs integration. The proposed multi-agent system consists of four types of agents: i) Distribution system operator (DSO) technical agent and ii) DSO market agents that both belong to the top layer of the hierarchy and their roles are to manage the distribution network by avoiding grid congestions and using congestion prices to coordinate the energy scheduled; iii) Electric vehicle virtual power plant agents are in the middle level of the hierarchy and their roles are to manage the charge process of the electric vehicles; iv) Electric vehicle agents are placed at the bottom layer of the hierarchy and they represent electric vehicle owners with different users’ profiles. To demonstrate the coordination behavior of the proposed system, a multi-agent simulation platform is developed based on the co-simulation environment of JACK, Matlab and GAMS. The aim of the multi-agent system is to simulate the collaborative (all agents contribute to achieve an optimized global performance) but also competitive environment (each agent will try to increase its utilities or reduce its costs).

Hybridizing local and generic information to model cropping system spatial distribution in an agricultural landscape

For quantitative water management, fine analysis of spatial and temporal interactions between cropping systems and water resources helps identify time and site-specific withdrawal situations. However, it is a methodological challenge to provide fine-resolution analyses at the scale of large watersheds used for crises management. In this study, we present a methodology based on multiple methods and mixed sources of information to finely model an agricultural landscape (AL) that represents the spatial distribution of cropping systems. Our approach is based on progressively hybridizing databases and local actors' and experts' knowledge to produce a spatially explicit and dynamic model. The Land Parcel Identification System database, which resulted from the European Common Agricultural Policy, is crucial for our method since it provides the spatial and temporal basis of our model (i.e., geographic delineation of islets and information about crop sequences). Local knowledge is used to identify factors determining spatial distribution of cropping systems and to build a generic model that simulates farmers' crop-management strategies. The model was qualitatively and quantitatively evaluated using a multi-agent simulation platform (MAELIA). We asked local experts on quantitative water management to evaluate the ability of the platform to reproduce intra- and inter-annual dynamics at different levels when using our model of the AL as input. The experts were satisfied with the results; they validated the predicted dynamics of the variables, and some discussed the objectivity of the values. We discuss the method’s contribution to current challenges in modeling large agricultural areas and the associated tradeoffs. The approach is promising for policy makers who wish to develop integrated, locally adapted land-management strategies.

An architecture for scalable simulation of systems of cognitive agents

Using purely agent-based platforms for any kind of simulation requires to address the following challenges: 1 scalability; 2 efficient memory management; 3 modelling. While dedicated professional simulation tools usually provide rich domain libraries and advanced visualisation techniques, and support the simulation of large scenarios, they do not allow for 'agentisation' of single components. We are trying to bridge this gap by developing a distributed, scalable multi-agent simulation platform, MASeRaTi, addressing the three problems mentioned above. It allows to plug-in both dedicated simulation tools as well as the agentisation of certain components of the system. We describe the system architecture, which consists of a lightweight kernel and an agent-modelling layer, on top of which applications reside. An evaluation of the platform is provided by: 1 a proof-of-concept implementation of the well-known cow scenario used in the multi-agent programming contest; 2 experimentally investigating scalability in comparison to the Jason platform.

Designing an Incentive Contract Menu for Sustaining the Electricity Market

This paper designs an incentive contract menu to achieve long-term stability for electricity prices in a day-ahead electricity market. A bi-level Stackelberg game model is proposed to search for the optimal incentive mechanism under a one-leader and multi-followers gaming framework. A multi-agent simulation platform was developed to investigate the effectiveness of the incentive mechanism using an independent system operator (ISO) and multiple power generating companies (GenCos). Further, a Q-learning approach was implemented to analyze and assess the response of GenCos to the incentive menu. Numerical examples are provided to demonstrate the effectiveness of the incentive contract.