Evacuation modeling is a promising measure to support decision making in scenarios such as flooding,explosion,terrorist attack and other emergency incidents.Given the special attention to the terrorist attack,we build...Evacuation modeling is a promising measure to support decision making in scenarios such as flooding,explosion,terrorist attack and other emergency incidents.Given the special attention to the terrorist attack,we build up an agent-based evacuation model in a railway station square under sarin terrorist attack to analyze such incident.Sarin dispersion process is described by Gaussian puff model.Due to sarin’s special properties of being colorless and odorless,we focus more on the modeling of agents’perceiving and reasoning process and use a Belief,Desire,Intention(BDI)architecture to solve the problem.Another contribution of our work is that we put forward a path planning algorithm which not only take distance but also comfort and threat factors into consideration.A series of simulation experiments demonstrate the ability of the proposed model and examine some crucial factors in sarin terrorist attack evacuation.Though far from perfect,the proposed model could serve to support decision making.展开更多
How can we foster and grow artificial societies so as to cause social properties to emerge that are logical, consistent with real societies, and are expected by design- ers? We propose a framework for fostering artif...How can we foster and grow artificial societies so as to cause social properties to emerge that are logical, consistent with real societies, and are expected by design- ers? We propose a framework for fostering artificial soci- eties using social learning mechanisms and social control ap- proaches. We present the application of fostering artificial so- cieties in parallel emergency management systems. Then we discuss social learning mechanisms in artificial societies, in- cluding observational learning, reinforcement learning, imi- tation learning, and advice-based learning. Furthermore, we discuss social control approaches, including social norms, social policies, social reputations, social commitments, and sanctions.展开更多
The Corona Virus Disease 2019(COVID-19)pandemic is still imposing a devastating impact on public health,the economy,and society.Predicting the development of epidemics and exploring the effects of various mitigation s...The Corona Virus Disease 2019(COVID-19)pandemic is still imposing a devastating impact on public health,the economy,and society.Predicting the development of epidemics and exploring the effects of various mitigation strategies have been a research focus in recent years.However,the spread simulation of COVID-19 in the dynamic social system is relatively unexplored.To address this issue,considering the outbreak of COVID-19 at Nanjing Lukou Airport in 2021,we constructed an artificial society of Nanjing Lukou Airport based on the Artificial societies,Computational experiments,and Parallel execution(ACP)approach.Specifically,the artificial society includes an environmental model,population model,contact networks model,disease spread model,and intervention strategy model.To reveal the dynamic variation of individuals in the airport,we first modeled the movement of passengers and designed an algorithm to generate the moving traces.Then,the mobile contact networks were constructed and aggregated with the static networks of staff and passengers.Finally,the complex dynamical network of contacts between individuals was generated.Based on the artificial society,we conducted large-scale computational experiments to study the spread characteristics of COVID-19 in an airport and to investigate the effects of different intervention strategies.Learned from the reproduction of the outbreak,it is found that the increase in cumulative incidence exhibits a linear growth mode,different from that(an exponential growth mode)in a static network.In terms of mitigation measures,promoting unmanned security checks and boarding in an airport is recommended,as to reduce contact behaviors between individuals and staff.展开更多
Large-scale artificial societies with millions or billions of agents call for high-performance parallel simulation.Prevailing supercomputers with thousands of CPUs and GPUs make it possible to carry out such simulatio...Large-scale artificial societies with millions or billions of agents call for high-performance parallel simulation.Prevailing supercomputers with thousands of CPUs and GPUs make it possible to carry out such simulation.The key is to distribute large-scale agents to massive cores of CPUs and GPUs properly for parallel computing with efficient communication and synchronization.For simplicity and efficiency,a modified discrete event system specification(DEVS)is proposed for large-scale artificial society modeling and parallelism is exploited in agent models because similar agents usually share similar behaviors.Through phased synchronization,a two-tier parallel simulation engine is designed with support of MPI and OpenCL where GPU is used as coprocessor.One-sided communication is used for reflection of remote simulation objects and message passing between processes.A general kernel function prototype is elaborately designed and conditionally compiled for execution on both CPU and GPU.An artificial society for epidemic study is used to test the performance on a supercomputer with 1024 CPU cores and 1792 GPU cores.The speedup reaches 3512 for even 2 billion agents with GPU acceleration which is far over 701 when only CPUs are used.It turns out feasible for parallel simulation of large-scale artificial society with GPU as coprocessor.展开更多
Cloud computing is attracting an increasing number of simulation applications running in the virtualized cloud data center.These applications are submitted to the cloud in the form of simulation jobs.Meanwhile,the man...Cloud computing is attracting an increasing number of simulation applications running in the virtualized cloud data center.These applications are submitted to the cloud in the form of simulation jobs.Meanwhile,the management and scheduling of simulation jobs are playing an essential role to offer efficient and high productivity computational service.In this paper,we design a management and scheduling service framework for simulation jobs in two-tier virtualization-based private cloud data center,named simulation execution as a service(SimEaaS).It aims at releasing users from complex simulation running settings,while guaranteeing the QoS requirements adaptively.Furthermore,a novel job scheduling algorithm named adaptive deadline-aware job size adjustment(ADaSA)algorithm is designed to realize high job responsiveness under QoS requirement for SimEaaS.ADaSA tries to make full use of the idle fragmentation resources by tuning the number of requested processes of submitted jobs in the queue adaptively,while guaranteeing that jobs’deadline requirements are not violated.Extensive experiments with trace-driven simulation are conducted to evaluate the performance of our ADaSA.The results show that ADaSA outperforms both cloud-based job scheduling algorithm KCEASY and traditional EASY in terms of response time(up to 90%)and bounded slow down(up to 95%),while obtains approximately equivalent deadline-missed rate.ADaSA also outperforms two representative moldable scheduling algorithms in terms of deadline-missed rate(up to 60%).展开更多
基金the National Natural Science Foundation of China under Grant Nos.71303252,61403402,61503402 and 71673292.
文摘Evacuation modeling is a promising measure to support decision making in scenarios such as flooding,explosion,terrorist attack and other emergency incidents.Given the special attention to the terrorist attack,we build up an agent-based evacuation model in a railway station square under sarin terrorist attack to analyze such incident.Sarin dispersion process is described by Gaussian puff model.Due to sarin’s special properties of being colorless and odorless,we focus more on the modeling of agents’perceiving and reasoning process and use a Belief,Desire,Intention(BDI)architecture to solve the problem.Another contribution of our work is that we put forward a path planning algorithm which not only take distance but also comfort and threat factors into consideration.A series of simulation experiments demonstrate the ability of the proposed model and examine some crucial factors in sarin terrorist attack evacuation.Though far from perfect,the proposed model could serve to support decision making.
文摘How can we foster and grow artificial societies so as to cause social properties to emerge that are logical, consistent with real societies, and are expected by design- ers? We propose a framework for fostering artificial soci- eties using social learning mechanisms and social control ap- proaches. We present the application of fostering artificial so- cieties in parallel emergency management systems. Then we discuss social learning mechanisms in artificial societies, in- cluding observational learning, reinforcement learning, imi- tation learning, and advice-based learning. Furthermore, we discuss social control approaches, including social norms, social policies, social reputations, social commitments, and sanctions.
基金supported by the National Natural Science Foundation of China(Nos.62173337,21808181 and 72071207)the Hunan Key Laboratory of Intelligent Decision-Making Technology for Emergency Management(No.2020TP1013)Humanity and Social Science Youth Foundation of Ministry of China(No.19YJCZH073).
文摘The Corona Virus Disease 2019(COVID-19)pandemic is still imposing a devastating impact on public health,the economy,and society.Predicting the development of epidemics and exploring the effects of various mitigation strategies have been a research focus in recent years.However,the spread simulation of COVID-19 in the dynamic social system is relatively unexplored.To address this issue,considering the outbreak of COVID-19 at Nanjing Lukou Airport in 2021,we constructed an artificial society of Nanjing Lukou Airport based on the Artificial societies,Computational experiments,and Parallel execution(ACP)approach.Specifically,the artificial society includes an environmental model,population model,contact networks model,disease spread model,and intervention strategy model.To reveal the dynamic variation of individuals in the airport,we first modeled the movement of passengers and designed an algorithm to generate the moving traces.Then,the mobile contact networks were constructed and aggregated with the static networks of staff and passengers.Finally,the complex dynamical network of contacts between individuals was generated.Based on the artificial society,we conducted large-scale computational experiments to study the spread characteristics of COVID-19 in an airport and to investigate the effects of different intervention strategies.Learned from the reproduction of the outbreak,it is found that the increase in cumulative incidence exhibits a linear growth mode,different from that(an exponential growth mode)in a static network.In terms of mitigation measures,promoting unmanned security checks and boarding in an airport is recommended,as to reduce contact behaviors between individuals and staff.
基金supported by the National Natural Science Foundation of China under grant 91024030.
文摘Large-scale artificial societies with millions or billions of agents call for high-performance parallel simulation.Prevailing supercomputers with thousands of CPUs and GPUs make it possible to carry out such simulation.The key is to distribute large-scale agents to massive cores of CPUs and GPUs properly for parallel computing with efficient communication and synchronization.For simplicity and efficiency,a modified discrete event system specification(DEVS)is proposed for large-scale artificial society modeling and parallelism is exploited in agent models because similar agents usually share similar behaviors.Through phased synchronization,a two-tier parallel simulation engine is designed with support of MPI and OpenCL where GPU is used as coprocessor.One-sided communication is used for reflection of remote simulation objects and message passing between processes.A general kernel function prototype is elaborately designed and conditionally compiled for execution on both CPU and GPU.An artificial society for epidemic study is used to test the performance on a supercomputer with 1024 CPU cores and 1792 GPU cores.The speedup reaches 3512 for even 2 billion agents with GPU acceleration which is far over 701 when only CPUs are used.It turns out feasible for parallel simulation of large-scale artificial society with GPU as coprocessor.
基金supported by Scientific Research Plan of National University of Defense Technology under Grant No.ZK-20-38National Key Research&Development(R&D)Plan under Grant No.2017YFC0803300+2 种基金the National Natural Science Foundation of China under Grant Nos.71673292,71673294,61503402 and 61673388National Social Science Foundation of China under Grant No.17CGL047Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion.
文摘Cloud computing is attracting an increasing number of simulation applications running in the virtualized cloud data center.These applications are submitted to the cloud in the form of simulation jobs.Meanwhile,the management and scheduling of simulation jobs are playing an essential role to offer efficient and high productivity computational service.In this paper,we design a management and scheduling service framework for simulation jobs in two-tier virtualization-based private cloud data center,named simulation execution as a service(SimEaaS).It aims at releasing users from complex simulation running settings,while guaranteeing the QoS requirements adaptively.Furthermore,a novel job scheduling algorithm named adaptive deadline-aware job size adjustment(ADaSA)algorithm is designed to realize high job responsiveness under QoS requirement for SimEaaS.ADaSA tries to make full use of the idle fragmentation resources by tuning the number of requested processes of submitted jobs in the queue adaptively,while guaranteeing that jobs’deadline requirements are not violated.Extensive experiments with trace-driven simulation are conducted to evaluate the performance of our ADaSA.The results show that ADaSA outperforms both cloud-based job scheduling algorithm KCEASY and traditional EASY in terms of response time(up to 90%)and bounded slow down(up to 95%),while obtains approximately equivalent deadline-missed rate.ADaSA also outperforms two representative moldable scheduling algorithms in terms of deadline-missed rate(up to 60%).