人群模拟

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Crowd simulation is the process of simulating the movement (or 模板:Vanchor) of a large number of entities or characters.[1] It is commonly used to create virtual scenes for visual media like films and video games, and is also used in crisis training,[2] architecture and urban planning,[3] and evacuation simulation.[4]

Crowd simulation is the process of simulating the movement (or ) of a large number of entities or characters. It is commonly used to create virtual scenes for visual media like films and video games, and is also used in crisis training, architecture and urban planning, and evacuation simulation.

人群模拟 Crowd simulation是对大量实体或人物的运动进行模拟的过程。[5]它常用于电影和视频游戏等视觉媒体中创建虚拟场景,也用于危机培训、[6]建筑和城市规划[7] 以及 人群疏散模拟 Evacuation Simulations[8]


Crowd simulation may focus on aspects that target different applications. For realistic and fast rendering of a crowd for visual media or virtual cinematography, reduction of the complexity of the 3D scene and image-based rendering are used,[9] while variations in appearance help present a realistic population.[10][11]

Crowd simulation may focus on aspects that target different applications. For realistic and fast rendering of a crowd for visual media or virtual cinematography, reduction of the complexity of the 3D scene and image-based rendering are used, while variations in appearance help present a realistic population.

人群模拟专注的方面可以针对不同应用。为了对 视觉媒体 visual media 虚拟摄影 virtual cinematography中的人群进行真实和快速渲染,可以减少 3D 场景的复杂性和基于图像的渲染,[12] 而外观的变化有助于展现一个真实的人群。[10][11]


In games and applications intended to replicate real-life human crowd movement, like in evacuation simulations, simulated agents may need to navigate towards a goal, avoid collisions, and exhibit other human-like behavior. Many crowd steering algorithms have been developed to lead simulated crowds to their goals realistically. Some more general systems are researched that can support different kinds of agents (like cars and pedestrians),[13] different levels of abstraction(like individual and continuum),[14] agents interacting with smart objects,[15] and more complex physical and social dynamics.[16]

In games and applications intended to replicate real-life human crowd movement, like in evacuation simulations, simulated agents may need to navigate towards a goal, avoid collisions, and exhibit other human-like behavior. Many crowd steering algorithms have been developed to lead simulated crowds to their goals realistically. Some more general systems are researched that can support different kinds of agents (like cars and pedestrians), different levels of abstraction(like individual and continuum), agents interacting with smart objects, and more complex physical and social dynamics.

在模拟真实人群运动的游戏和应用程序,比如人群疏散模拟中, 模拟的主体 Simulated Agents可能需要到达某个目标,避免碰撞,并表现出其他类似人的行为。现在已经有很多种 人群引导算法 Crowd Steering Algorithms能实际地引导模拟人群到达他们的目标。一些更普通的系统被研发出来支持不同的主体类型(如汽车和行人),[17]不同的抽象层次(如个体和 连续体 Continuum),[18]可以和智能对象交互的主体,[19]以及更复杂的物理和社会动态。[20]



History 历史

There has always been a deep-seated interest in the understanding and gaining control of motional and behavior of crowds of people. Many major advancements have taken place since the beginnings of research within the realm of crowd simulation. Evidently many new findings are continually made and published following these which improve the scalability, flexibility, applicability, and realism of simulations:

There has always been a deep-seated interest in the understanding and gaining control of motional and behavior of crowds of people. Many major advancements have taken place since the beginnings of research within the realm of crowd simulation. Evidently many new findings are continually made and published following these which improve the scalability, flexibility, applicability, and realism of simulations:

理解和控制人群的运动和行为一直是人们的兴趣所在。人群模拟领域的研究开始以来,人们已经取得了许多重大进展。显然,许多新发现正不断地被提出、被发表。这些发现提高了仿真模拟的可扩展性、灵活性、适用性和真实性 :


In 1987, behavioral animation was introduced and developed by Craig Reynolds.[21] He had simulated flocks of birds alongside schools of fish for the purpose of studying group intuition and movement. All agents within these simulations were given direct access to the respective positions and velocities of their surrounding agents. The theorization and study set forth by Reynolds was improved and built upon in 1994 by Xiaoyuan Tu, Demetri Terzopoulos and Radek Grzeszczuk.[22] The realistic quality of simulation was engaged with as the individual agents were equipped with synthetic vision and a general view of the environment within which they resided, allowing for a perceptual awareness within their dynamic habitats.

In 1987, behavioral animation was introduced and developed by Craig Reynolds. He had simulated flocks of birds alongside schools of fish for the purpose of studying group intuition and movement. All agents within these simulations were given direct access to the respective positions and velocities of their surrounding agents. The theorization and study set forth by Reynolds was improved and built upon in 1994 by Xiaoyuan Tu, Demetri Terzopoulos and Radek Grzeszczuk. The realistic quality of simulation was engaged with as the individual agents were equipped with synthetic vision and a general view of the environment within which they resided, allowing for a perceptual awareness within their dynamic habitats.

1987年,克雷格·雷诺兹 Craig Reynolds引入并发展了 行为动画 behavioral animation[23]为了研究群体直觉和运动,他模拟了一群鸟和一群鱼。在这些模拟中的所有主体都可以直接访问它们周围各个主体的位置和速度。雷诺兹建立的理论和研究是在Xiaoyuan Tu、特佐普洛斯 Demetri Terzopoulos和格兹兹丘克 Radek Grzeszczuk1994年的研究之上改进和发展的。.[24] 该模拟的真实性是有保证的,因为个体主体拥有合成视觉并对它们的居住环境有总体了解,从而可以对动态的栖息地有感性的认识。

 --嘉树讨论) The realistic quality of simulation... 这一句话分成两句看,并且加入了一些个人理解,不知含义是否正确
 --WildBoar讨论) 这句不太通
 
 --Vicky讨论) as后面引导了一个原因状语从句


Initial research in the field of crowd simulation began in 1997 with Daniel Thalmann's supervision of Soraia Raupp Musse's PhD thesis. These two present a new model of crowd behavior in order to create a simulation of generic populations.[25] Here a relation is drawn between the autonomous behavior of the individual within the crowd and the emergent behavior originating from this.[26]

Initial research in the field of crowd simulation began in 1997 with Daniel Thalmann's supervision of Soraia Raupp Musse's PhD thesis. These two present a new model of crowd behavior in order to create a simulation of generic populations. Here a relation is drawn between the autonomous behavior of the individual within the crowd and the emergent behavior originating from this.

最初的人群模拟领域研究始于1997年丹尼尔·塔尔曼 Daniel Thalmann指导苏拉娅·劳普·米塞尔 Soraia Raupp Musse的博士论文。他们两人提出了一个新的人群行为模型,以创建一个一般人群的仿真模拟。[27] 在这个模型中,他们给出了群体中个体的自主行为和由此产生的行为之间的一种关系。[28]


In 1999, individualistic navigation began its course within the realm of crowd simulation via continued research of Craig Reynolds.[29] Steering behaviors are proven to play a large role in the process of automating agents within a simulation. Reynolds states the processes of low-level locomotion to be dependent and reliant on mid-level steering behaviors and higher-level goal states and path finding strategies. Building off of the advanced work of Reynolds, Musse and Thalmann began to study the modeling of real time simulations of these crowds, and their applications to human behavior. The control of human crowds was designated as a hierarchical organization with levels of autonomy amongst agents. This marks the beginnings of modeling individual behavior in its most elementary form on humanoid agents.[30]

In 1999, individualistic navigation began its course within the realm of crowd simulation via continued research of Craig Reynolds. Steering behaviors are proven to play a large role in the process of automating agents within a simulation. Reynolds states the processes of low-level locomotion to be dependent and reliant on mid-level steering behaviors and higher-level goal states and path finding strategies. Building off of the advanced work of Reynolds, Musse and Thalmann began to study the modeling of real time simulations of these crowds, and their applications to human behavior. The control of human crowds was designated as a hierarchical organization with levels of autonomy amongst agents. This marks the beginnings of modeling individual behavior in its most elementary form on humanoid agents.

1999年,通过雷诺兹的持续研究, 个人主义导航 individualistic navigation开始了它在人群模拟领域的进程。[31]仿真中, 指导行为Steering Behaviors在自动化主体过程中起着重要的作用。雷诺兹指出,低水平运动的过程依赖中等水平的指导行为和高水平的目标状态以及 路径寻找策略 Path Finding Strategies。在 雷诺兹工作的基础之上,米塞尔和塔尔曼开始研究人群的实时模拟模型,以及它们在人类行为中的应用。对人群的控制被指定为一个具有不同主体自治水平的等级组织。这标志着在类人主体上以最基本的形式建立个体行为模型的开始。[32]


Coinciding with publications regarding human behavior models and simulations of group behaviors, Matt Anderson, Eric McDaniel, and Stephen Chenney's proposal of constraints on behavior[33] gained popularity. The positioning of constraints on group animations was presented to be able to be done at any time within the simulation. This process of applying constraints to the behavioral model is undergone in a two-fold manner, by first determining the initial set of goal trajectories coinciding with the constraints, and then applying behavioral rules to these paths to select those which do not violate them.

Coinciding with publications regarding human behavior models and simulations of group behaviors, Matt Anderson, Eric McDaniel, and Stephen Chenney's proposal of constraints on behavior gained popularity. The positioning of constraints on group animations was presented to be able to be done at any time within the simulation. This process of applying constraints to the behavioral model is undergone in a two-fold manner, by first determining the initial set of goal trajectories coinciding with the constraints, and then applying behavioral rules to these paths to select those which do not violate them.

和人类行为模型和群体行为模拟的论文发表恰巧同一时期,马特•安德森 Matt Anderson、埃里克•麦克丹尼尔 Eric McDaniel和史蒂芬·切尼Stephen Chenney关于行为约束的研究计划[34]大受欢迎。他们展示了对 群体动画 Group Animations约束的定位能够在程序模拟中的任何时候完成。将约束应用到行为模型的过程分为两步: 首先,确定与约束条件一致的初始的 目标轨迹 goal trajectories集;然后,将行为规则应用到这些路径上,去选择那些不违反约束条件的路径。


Correlating and building off of the findings proposed in his work with Musse, Thalmann, working alongside Bratislava Ulicny and Pablo de Heras Ciechomski, proposed a new model which allowed for interactive authoring of agents at the level of an individual, a group of agents and the entirety of a crowd. A brush metaphor is introduced to distribute, model and control crowd members in real-time with immediate feedback.[35]

Correlating and building off of the findings proposed in his work with Musse, Thalmann, working alongside Bratislava Ulicny and Pablo de Heras Ciechomski, proposed a new model which allowed for interactive authoring of agents at the level of an individual, a group of agents and the entirety of a crowd. A brush metaphor is introduced to distribute, model and control crowd members in real-time with immediate feedback.

塔尔曼基于他和米塞尔合作中提出的研究成果,和布拉迪斯拉发·乌利奇尼 Bratislava Ulicny以及巴勃罗·德·赫拉斯·切科姆斯基 Pablo de Heras Ciechomski提出了一种新的模型。这种模型允许在个人、一个群组和整个群体的层面上进行主体的 交互式创作 Interactive Authoring。他们引入了一个刷子隐喻,在即时反馈的情况下来进行实时分配、建模和控制人群成员。.[36]


Crowd dynamics 人群动态

One of the major goals in crowd simulation is to steer crowds realistically and recreate human dynamic behaviors.

One of the major goals in crowd simulation is to steer crowds realistically and recreate human dynamic behaviors.

人群模拟的主要目标之一是真实地引导人群并重现人类的动态行为。


There exists several overarching approaches to crowd simulation and AI, each one providing advantages and disadvantages based on crowd size and time scale. Time scale refers to how the objective of the simulation also affects the length of the simulation. For example, researching social questions such as how ideologies are spread amongst a population will result in a much longer running simulation since such an event can span up to months or years. Using those two characteristics, researchers have attempted to apply classifications to better evaluate and organize existing crowd simulators.[37]

There exists several overarching approaches to crowd simulation and AI, each one providing advantages and disadvantages based on crowd size and time scale. Time scale refers to how the objective of the simulation also affects the length of the simulation. For example, researching social questions such as how ideologies are spread amongst a population will result in a much longer running simulation since such an event can span up to months or years. Using those two characteristics, researchers have attempted to apply classifications to better evaluate and organize existing crowd simulators.

人群模拟和人工智能有几种主要的方法,每种方法根据人群规模和时间尺度具有不同的优缺点。时间尺度是指仿真的目标如何也会影响仿真的时间长度。例如,研究诸如意识形态在人群中的分布等社会问题将导致模拟运行的时间更长,因为意识形态分布这样的事件可能长达数月或数年。利用这两个特点,研究人员已经试图分类现有的人群模拟器,以更好地评估和组织它们。[38]


Flow-based Approach
Flow based crowd simulations focus on the crowd as a whole rather than its components. As such individuals do not have any distinctive behaviors that occur due to input from their surroundings and behavioral factors are largely reduced.[39] This model is mainly used to estimate the flow of movement of a large and dense crowd in a given environment. Best used in studying large crowd, short time objectives.

Flow-based Approach: Flow based crowd simulations focus on the crowd as a whole rather than its components. As such individuals do not have any distinctive behaviors that occur due to input from their surroundings and behavioral factors are largely reduced. This model is mainly used to estimate the flow of movement of a large and dense crowd in a given environment. Best used in studying large crowd, short time objectives.

基于流的方法 Flow-based Approach: 基于流的人群模拟关注于人群作为一个整体,而不是人群的各种组成部分。这样人群中的个体没有任何独特的行为是由于他们周围的环境引起的,行为因素在很大程度上被减少了。[39]该模型主要用于估计给定环境下大型密集人群的运动流 the flow of movement。最适用于研究人群大、时间短的目标。


Entity-based Approach
Models that implement a set of physical, predefined, and global laws meant to simulate social/psychological factors that occur in individuals that are a part of a crowd fall under this category. Entities in this case do not have the capacity to, in a sense, think for themselves. All movements are determined by the global laws being enforced on them. Simulations that use this model often do so to research crowd dynamics such as jamming and flocking. Small to medium-sized crowds with short term objectives fit this approach best.

Entity-based Approach: Models that implement a set of physical, predefined, and global laws meant to simulate social/psychological factors that occur in individuals that are a part of a crowd fall under this category. Entities in this case do not have the capacity to, in a sense, think for themselves. All movements are determined by the global laws being enforced on them. Simulations that use this model often do so to research crowd dynamics such as jamming and flocking. Small to medium-sized crowds with short term objectives fit this approach best.

基于实体的方法 Entity-based Approach: 属于这种类别方法的模型会实施一系列物理的、预先确定的整体规则,这些规则旨在模拟发生在属于群体的个人身上的社会/心理因素。在这类模型中,实体在某种意义上没有自己思考的能力。他们所有运动都是由整体规则决定的。这个模型的仿真模拟经常用来研究人群动态,例如拥挤jamming和聚集。中小型群体和较短时间的目标适合使用这种方法。


Agent-based Approach
Characterized by autonomous, interacting individuals. Each agent of a crowd in this approach, is given a degree of intelligence; they can react to each situation on their own based on a set of decision rules. Information used to decide on an action is obtained locally from the agent's' surroundings. Most often, this approach is used for simulating realistic crowd behavior as the researcher is given complete freedom to implement any behaviors.

Agent-based Approach: Characterized by autonomous, interacting individuals. Each agent of a crowd in this approach, is given a degree of intelligence; they can react to each situation on their own based on a set of decision rules. Information used to decide on an action is obtained locally from the agent's' surroundings. Most often, this approach is used for simulating realistic crowd behavior as the researcher is given complete freedom to implement any behaviors.

基于主体的方法 Agent-based Approach: 该方法的特点是拥有自治的、互动的个体。在这种方法中,人群中的每个主体都被赋予了一定程度的智能; 他们可以根据一组决策规则自行对每个情况做出反应。主体用来决定一个行动的信息是从其周围获得的局部信息。这种方法最常见是被用来模拟现实的人群行为,因为研究人员可以完全自由地执行任何行为。


Particle systems 粒子系统

One way to simulate virtual crowds is to use a particle system. Particle systems were first introduced in computer graphics by W. T. Reeves in 1983.[40] A particle system is a collection of a number of individual elements or particles. Each particle is able to act autonomously and is assigned a set of physical attributes (such as color, size and velocity).

One way to simulate virtual crowds is to use a particle system. Particle systems were first introduced in computer graphics by W. T. Reeves in 1983. A particle system is a collection of a number of individual elements or particles. Each particle is able to act autonomously and is assigned a set of physical attributes (such as color, size and velocity).

一种模拟虚拟人群的方法是使用粒子系统 Particle System。1983年,W·T·里夫斯 W. T. Reeves 在计算机图形学 Computer Graphics中首次引入了粒子系统。[41]粒子系统是很多单个元素或粒子的集合。每个粒子都能够自主行动,并被赋予一组物理属性(如颜色、大小和速度)。


A particle system is dynamic, in that the movements of the particles change over time. A particle system's movement is what makes it so desirable and easy to implement. Calculating the movements of these particles takes very little time. It simply involves physics: the sum of all the forces acting on a particle determines its motion. Forces such as gravity, friction and force from a collision, and social forces like the attractive force of a goal.

A particle system is dynamic, in that the movements of the particles change over time. A particle system's movement is what makes it so desirable and easy to implement. Calculating the movements of these particles takes very little time. It simply involves physics: the sum of all the forces acting on a particle determines its motion. Forces such as gravity, friction and force from a collision, and social forces like the attractive force of a goal.

粒子系统是动态的,因为粒子的运动随着时间而改变。粒子系统的运动使这种方法很理想,且易于实现。计算这些粒子的运动只需要很少的时间。它仅仅涉及到物理学: 作用在一个粒子上的所有力的总和决定了它的运动。力包括了诸如重力、摩擦力、碰撞产生的力,以及诸如目标的吸引力之类的社会力。


Usually each particle has a velocity vector and a position vector, containing information about the particle's current velocity and position respectively. The particles next position is calculated by adding its velocity vector to its position vector. A very simple operation (again why particle systems are so desirable). Its velocity vector changes over time, in response to the forces acting on the particle. For example, a collision with another particle will cause it to change direction.

Usually each particle has a velocity vector and a position vector, containing information about the particle's current velocity and position respectively. The particles next position is calculated by adding its velocity vector to its position vector. A very simple operation (again why particle systems are so desirable). Its velocity vector changes over time, in response to the forces acting on the particle. For example, a collision with another particle will cause it to change direction.

通常每个粒子都有一个速度矢量和一个位置矢量,分别包含粒子当前的速度和位置信息。将粒子的速度矢量加到其位置矢量上,从而计算粒子的下一个位置。这是一个非常简单的操作(并再次说明了为什么粒子系统如此理想)。速度矢量在作用力影响下随时间变化。例如,与另一个粒子的碰撞将导致一个粒子改变方向。


Particles systems have been widely used in films for effects such as explosions, for water effects in the 2000 movie The Perfect Storm, and simulated gas in the 1994 film the Mask.

Particles systems have been widely used in films for effects such as explosions, for water effects in the 2000 movie The Perfect Storm, and simulated gas in the 1994 film the Mask.

粒子系统已经广泛应用于电影,如制作爆炸特效。具体的例子包括2000年的电影《完美风暴 The Perfect Storm》的水特效,以及1994年的电影《面具 Mask》的模拟气体特效等。


Particles systems, however, do have some drawbacks. It can be a bad idea to use a particle system to simulate agents in a crowd that the director will move on command, as determining which particles belong to the agent and which do not is very difficult.

Particles systems, however, do have some drawbacks. It can be a bad idea to use a particle system to simulate agents in a crowd that the director will move on command, as determining which particles belong to the agent and which do not is very difficult.

然而,粒子系统确实有一些缺点。使用粒子系统模拟那些领导会根据指令行动行动的人群中的主体,可能是个坏主意,因为确定哪些粒子属于主体,哪些不属于主体是非常困难的。

--嘉树讨论) crowd that the director will move on command 不确定是否是“根据领导的指令统一行动的人群”这个意思

--WildBoar讨论) crowd that the director will move on command 领导会根据指令行动的人群

Algorithm by Patil and Van Den Berg, 帕蒂尔 Patil和范登博格 Van Den Berg的算法

This algorithm was designed for relatively simplistic crowds, where each agent in the crowd only desires to get to its own goal destination while also avoiding obstacles.[42] This algorithm could be used for simulating a crowd in Times Square.

This algorithm was designed for relatively simplistic crowds, where each agent in the crowd only desires to get to its own goal destination while also avoiding obstacles. This algorithm could be used for simulating a crowd in Times Square.

帕蒂尔 Patil和范登博格 Van Den Berg的算法是针对相对简单的群体设计的,群体中的每个主体只希望到达自己的目的地,同时避开障碍物。[43] 这个算法可以用来模拟时代广场 Times Square的人群。


Patils algorithm's most important and distinctive feature is that it utilizes the concept of navigation fields for directing agents. This is different from a guidance field; a guidance field is an area around the agent in which the agent is capable of "seeing"/detecting information. Guidance fields are typically used for avoiding obstacles, dynamic obstacles (obstacles that move) in particular. Every agent possesses its own guidance field. A navigation field, on the other hand, is a vector field which calculates the minimum cost path for every agent so that every agent arrives at its own goal position.

Patils algorithm's most important and distinctive feature is that it utilizes the concept of navigation fields for directing agents. This is different from a guidance field; a guidance field is an area around the agent in which the agent is capable of "seeing"/detecting information. Guidance fields are typically used for avoiding obstacles, dynamic obstacles (obstacles that move) in particular. Every agent possesses its own guidance field. A navigation field, on the other hand, is a vector field which calculates the minimum cost path for every agent so that every agent arrives at its own goal position.

帕蒂尔算法最重要且具有区分性的特征是它利用了 导航场 Navigation Fields的概念来指导主体。这不同于 指导场 Guidance Field; 指导场是主体周围的一个区域,主体可以在其中“看到”或检测到信息。指导场通常用于避开障碍物,特别是动态障碍物(移动的障碍物)。每个主体都有自己的指导场。另一方面,导航场是一个 向量场 vector field,它为每个主体计算 最小代价路径 minimum cost path,使每个主体到达自己的目标位置。


The navigation field can only be used properly when a path exists from every free (non-obstacle) position in the environment to one of the goal positions. The navigation field is computed using coordinates of the static objects in the environment, goal positions for each agent, and the guidance field for each agent. In order to guarantee that every agent reaches its own goal the navigation field must be free of local minima, except for the presence of sinks at the specified goals.

The navigation field can only be used properly when a path exists from every free (non-obstacle) position in the environment to one of the goal positions. The navigation field is computed using coordinates of the static objects in the environment, goal positions for each agent, and the guidance field for each agent. In order to guarantee that every agent reaches its own goal the navigation field must be free of local minima, except for the presence of sinks at the specified goals.

只有当从环境中的每一个 自由(无障碍)位置 free (non-obstacle) position到某一目标位置的路径存在时,导航场才能被正确使用。导航场是使用环境中静态对象的坐标、每个主体的目标位置和指导场计算的。为了保证每个主体达到自己的目标,导航场必须不受 局部极小值 local minima的限制,除非在特定目标处存在 汇 sinks


The running time of computing the navigation field is [math]\displaystyle{ O(m*n*log(mn)) }[/math], where m × n is the grid dimension (similar to Dijkstra's algorithm). Thus, the algorithm is only dependent on the grid resolution and not dependent on the number of agents in the environment. However, this algorithm has a high memory cost.

The running time of computing the navigation field is [math]\displaystyle{ O(m*n*log(mn)) }[/math], where m × n is the grid dimension (similar to Dijkstra's algorithm). Thus, the algorithm is only dependent on the grid resolution and not dependent on the number of agents in the environment. However, this algorithm has a high memory cost.

计算导航场的运行时间的公式是

[math]\displaystyle{ O(m*n*log(mn)) }[/math]

其中 m × n 是网格维数(类似于迪杰斯特拉算法)。因此,该算法仅依赖于网格分辨率,而不依赖于环境中的主体数量。但是,这种算法的内存消耗很大。


Individual behavior modelling 个体行为建模

One set of techniques for AI-based crowd simulation is to model crowd behavior by advanced simulation of individual agent motivations and decision-making. Generally, this means each agent is assigned some set of variables that measure various traits or statuses such as stress, personality, or different goals. This results in more realistic crowd behavior though may be more computationally intensive than simpler techniques.

One set of techniques for AI-based crowd simulation is to model crowd behavior by advanced simulation of individual agent motivations and decision-making. Generally, this means each agent is assigned some set of variables that measure various traits or statuses such as stress, personality, or different goals. This results in more realistic crowd behavior though may be more computationally intensive than simpler techniques.

基于AI的群体建模的技术之一,是通过对个体动机和决策的高级模拟来建立群体行为模型。一般来说,这意味着每个主体都被分配了一组变量来衡量不同的特征或状态,比如压力、人格或不同的目标。这导致更真实的群体行为,虽然可能比使用简单的技术计算量更大。


Personality-based models 基于人格的模型

One method of creating individualistic behavior for crowd agents is through the use of personality traits.[44] Each agent may have certain aspects of their personality tuned based on a formula that associates aspects such as aggressiveness or impulsiveness with variables that govern the agents' behavior. One way this association can be found is through a subjective study in which agents are randomly assigned values for these variables and participants are asked to describe each agent in terms of these personality traits. A regression may then be done to determine a correlation between these traits and the agent variables. The personality traits can then be tuned and have an appropriate effect on agent behavior.

One method of creating individualistic behavior for crowd agents is through the use of personality traits. Each agent may have certain aspects of their personality tuned based on a formula that associates aspects such as aggressiveness or impulsiveness with variables that govern the agents' behavior. One way this association can be found is through a subjective study in which agents are randomly assigned values for these variables and participants are asked to describe each agent in terms of these personality traits. A regression may then be done to determine a correlation between these traits and the agent variables. The personality traits can then be tuned and have an appropriate effect on agent behavior.

为人群的主体创建个体行为模型的一种方法是使用人格特质。[45] 每个主体可能会根据一个公式调整他们个性的某些方面,这个公式将侵略性或冲动性等方面与控制主体行为的变量联系起来。发现这种联系的一个方法是通过一个主观的研究,在这个研究中主体被随机分配这些变量的值,参与者被要求用这些人格特征来描述每个主体。然后可以进行回归,以确定这些特征和主体变量之间的相关性。人格特征可以被调整,并对主体行为产生适当的影响。


The OCEAN personality model has been used to define a mapping between personality traits and crowd simulation parameters. Automating crowd parameter tuning with personality traits provides easy authoring of scenarios with heterogeneous crowds.[46]

The OCEAN personality model has been used to define a mapping between personality traits and crowd simulation parameters. Automating crowd parameter tuning with personality traits provides easy authoring of scenarios with heterogeneous crowds.

五大人格特征模型 Big Five personality traits/OCEAN personality model定义了人格特质与人群模拟参数之间的映射关系。自动调整人群的人格特征参数使得能够在错杂的人群中简便地创作场景。[47]


Stress-based model 基于压力的模型

The behavior of crowds in high-stress situations can be modeled using General Adaptation Syndrome theory.[48] Agent behavior is affected by various stressors from their environment categorized into four prototypes: time pressure, area pressure, positional stressors, and interpersonal stressors, each with associated mathematical models.

The behavior of crowds in high-stress situations can be modeled using General Adaptation Syndrome theory.l Agent behavior is affected by various stressors from their environment categorized into four prototypes: time pressure, area pressure, positional stressors, and interpersonal stressors, each with associated mathematical models.

高压状态下的人群行为可以用 一般适应综合症理论 General Adaptation Syndrome theory来模拟。[49] 主体行为受到来自所处环境的各种压力源的影响,包括 时间压力 time pressure 区域压力 area pressure 位置压力 positional stressors 人际压力 interpersonal stressors四个原型,每个原型都有相关的数学模型。


Time pressure refers to stressors related to a time limit in reaching a particular goal. An example would be a street crossing with a timed walk signal or boarding a train before the doors are closed. This prototype is modeled by the following formula:

Time pressure refers to stressors related to a time limit in reaching a particular goal. An example would be a street crossing with a timed walk signal or boarding a train before the doors are closed. This prototype is modeled by the following formula:

时间压力是和达到特定目标的时间限制有关的压力源。例如,一个设有限时步行信号的十字路口,或在车门关闭前上车。这个原型是按照以下公式建模的:


[math]\displaystyle{ I_t = max(t_e - t_a, 0) }[/math]


where [math]\displaystyle{ I_t }[/math] is the intensity of the time pressure as a function of the estimated time to reach the goal [math]\displaystyle{ t_e }[/math] and a time constraint [math]\displaystyle{ t_a }[/math].

where [math]\displaystyle{ I_t }[/math] is the intensity of the time pressure as a function of the estimated time to reach the goal [math]\displaystyle{ t_e }[/math] and a time constraint [math]\displaystyle{ t_a }[/math].

其中 [math]\displaystyle{ I_t }[/math] 是时间压力强度,它是关于达到目标的估计时间[math]\displaystyle{ t_e }[/math]和时间限制 [math]\displaystyle{ t_a }[/math]的函数。


Area pressure refers to stessors as a result of an environmental condition. Examples would be noise or heat in an area. The intensity of this stressor is constant over a particular area and is modeled by the following formula:

Area pressure refers to stessors as a result of an environmental condition. Examples would be noise or heat in an area. The intensity of this stressor is constant over a particular area and is modeled by the following formula:

区域压力是指由于环境条件而产生的压力。例如某个地区的噪音或热量。这种压力源的强度在某一特定地区是恒定的,其模型如下:


[math]\displaystyle{ I_a = \begin{cases} c & \text{if }p_a\in A \\ 0 & \text{if }p_a\not\in A \end{cases} }[/math]


where [math]\displaystyle{ I_a }[/math] is the intensity of the area pressure, [math]\displaystyle{ p_a }[/math] is the position of the agent in an area [math]\displaystyle{ A }[/math], and [math]\displaystyle{ c }[/math] is a constant.

where [math]\displaystyle{ I_a }[/math] is the intensity of the area pressure, [math]\displaystyle{ p_a }[/math] is the position of the agent in an area [math]\displaystyle{ A }[/math], and [math]\displaystyle{ c }[/math] is a constant.

其中[math]\displaystyle{ I_a }[/math]是区域压力强度,[math]\displaystyle{ p_a }[/math] 是主体在A区的位置,c是一个常数。


Positional stressors refer to stressors associated with a local source of stress. The intensity of this stressor increases as an agent approaches the source of the stress. An example would be a fire or a dynamic object such as an assailant. It can be modeled by the following formula:

Positional stressors refer to stressors associated with a local source of stress. The intensity of this stressor increases as an agent approaches the source of the stress. An example would be a fire or a dynamic object such as an assailant. It can be modeled by the following formula:

位置压力是指与局部压力源相关的压力源。这种压力源的强度随着主体接近压力源而增加。一个例子就是火灾,或运动的物体比如攻击者。它可以用下面的公式来模拟:


[math]\displaystyle{ I_p = \lVert p_a - p_s \rVert }[/math]


where [math]\displaystyle{ I_p }[/math] is the intensity of the positional stressor, [math]\displaystyle{ p_a }[/math] is the position of the agent and [math]\displaystyle{ p_s }[/math] is the position of the stressor. Alternatively, stressors that generate high stress over a large area (such as a fire) can be modeled using a Gaussian distribution with standard deviation [math]\displaystyle{ \sigma }[/math]:

where [math]\displaystyle{ I_p }[/math] is the intensity of the positional stressor, [math]\displaystyle{ p_a }[/math] is the position of the agent and [math]\displaystyle{ p_s }[/math] is the position of the stressor. Alternatively, stressors that generate high stress over a large area (such as a fire) can be modeled using a Gaussian distribution with standard deviation [math]\displaystyle{ \sigma }[/math]:

其中 [math]\displaystyle{ I_p }[/math] 是位置压力源的强度,[math]\displaystyle{ p_a }[/math] 是主体的位置,[math]\displaystyle{ p_s }[/math] 是压力源的位置。另外,在一个大的区域产生高压力的压力源(如火灾),可以用一个带有正态分布的标准差[math]\displaystyle{ \sigma }[/math]的模型来模拟:


[math]\displaystyle{ I_p = \mathcal{N}(p_a - p_s, \sigma) }[/math]


Interpersonal stressors are stressors as a result of crowding by nearby agents. It can be modeled by the following formula:

Interpersonal stressors are stressors as a result of crowding by nearby agents. It can be modeled by the following formula:

人际压力源是由邻近主体的拥挤程度而产生的压力源。它可以用下面的公式来模拟:


[math]\displaystyle{ I_i = max(n_c - n_p, 0) }[/math]


where [math]\displaystyle{ I_i }[/math] is the intensity of the interpersonal stressor, [math]\displaystyle{ n_c }[/math] is the current number of neighbors within a unit space and [math]\displaystyle{ n_p }[/math] is the preferred number of neighbors within a unit space for that particular agent.

where [math]\displaystyle{ I_i }[/math] is the intensity of the interpersonal stressor, [math]\displaystyle{ n_c }[/math] is the current number of neighbors within a unit space and [math]\displaystyle{ n_p }[/math] is the preferred number of neighbors within a unit space for that particular agent.

其中[math]\displaystyle{ I_i }[/math]表示人际压力源的强度,[math]\displaystyle{ n_c }[/math] 表示当前单位空间内邻居的数量,[math]\displaystyle{ n_p }[/math] 表示单位空间内该特定主体理想的邻居数量。


The perceived stress follows Steven's Law and is modeled by the formula:

The perceived stress follows Steven's Law and is modeled by the formula:

感知到的压力遵循史蒂芬定律 Steven's Law ,并可以用下面的公式模拟:


[math]\displaystyle{ \psi(I) = kI^n }[/math]


where [math]\displaystyle{ \psi(I) }[/math] is the perceived stress for a stress level [math]\displaystyle{ I }[/math], [math]\displaystyle{ k }[/math] is a scale factor, and [math]\displaystyle{ n }[/math] is an exponent depending on the stressor type.

where [math]\displaystyle{ \psi(I) }[/math] is the perceived stress for a stress level [math]\displaystyle{ I }[/math], [math]\displaystyle{ k }[/math] is a scale factor, and [math]\displaystyle{ n }[/math] is an exponent depending on the stressor type.

其中[math]\displaystyle{ \psi(I) }[/math]是感知到的压力水平,[math]\displaystyle{ I }[/math]是客观的压力水平,[math]\displaystyle{ k }[/math] 是一个比例系数,[math]\displaystyle{ n }[/math]是一个取决于压力源类型的指数。


An agent's stress response can be found with the following formula:

An agent's stress response can be found with the following formula:

一个主体的压力反应可以通过以下公式得出:


[math]\displaystyle{ {dS \over dt} = \begin{cases} \alpha & \text{if } \psi \gt S \\ (-\alpha \leq {d\psi \over dt} \leq \alpha) & \text{if } \psi = S\\ -\alpha & \text{if } \psi \lt S\end{cases} }[/math]


where [math]\displaystyle{ S }[/math] is the stress response capped at a maximum value of [math]\displaystyle{ \beta }[/math] and [math]\displaystyle{ \alpha }[/math] is the maximum rate at which an agent's stress response can change.

where [math]\displaystyle{ S }[/math] is the stress response capped at a maximum value of [math]\displaystyle{ \beta }[/math] and [math]\displaystyle{ \alpha }[/math] is the maximum rate at which an agent's stress response can change.

其中,[math]\displaystyle{ S }[/math]是压力反应,它受到最大[math]\displaystyle{ \beta }[/math]值的限制,[math]\displaystyle{ \alpha }[/math]是主体的压力反应可以改变的最大速率。


Examples of notable crowd AI simulation can be seen in New Line Cinema's The Lord of the Rings films, where AI armies of thousands of characters battle each other. This crowd simulation was done using Weta Digital's Massive software.

Examples of notable crowd AI simulation can be seen in New Line Cinema's The Lord of the Rings films, where AI armies of thousands of characters battle each other. This crowd simulation was done using Weta Digital's Massive software.

值得注意的AI模拟例子可以在新线电影公司 New Line Cinema的《指环王 Lord of the Rings》系列电影中看到。在这部电影中,成千上万的AI角色互相争斗。这个人群模拟是用威塔数码公司 Weta Digital的Massive软件完成的。


Sociology 社会学

Crowd simulation can also refer to simulations based on group dynamics and crowd psychology, often in public safety planning. In this case, the focus is just the behavior of the crowd, and not the visual realism of the simulation. Crowds have been studied as a scientific interest since the end of the 19th Century. A lot of research has focused on the collective social behavior of people at social gatherings, assemblies, protests, rebellions, concerts, sporting events and religious ceremonies. Gaining insight into natural human behavior under varying types of stressful situations will allow better models to be created which can be used to develop crowd controlling strategies.

Crowd simulation can also refer to simulations based on group dynamics and crowd psychology, often in public safety planning. In this case, the focus is just the behavior of the crowd, and not the visual realism of the simulation. Crowds have been studied as a scientific interest since the end of the 19th Century. A lot of research has focused on the collective social behavior of people at social gatherings, assemblies, protests, rebellions, concerts, sporting events and religious ceremonies. Gaining insight into natural human behavior under varying types of stressful situations will allow better models to be created which can be used to develop crowd controlling strategies.

人群模拟也可以指基于群体动力学 Group Dynamics群体心理学 Crowd Psychology的模拟,通常用于公共安全规划 Public Safety Planning。在这种情况下,研究的焦点只是人群的行为,而不是模拟的视觉真实性。自19世纪末以来,人们一直将人群作为一种科学兴趣进行研究。许多研究都关注人们在社会聚会、集会、抗议、叛乱、音乐会、体育赛事和宗教仪式上的集体社会行为。了解不同类型压力情况下的人类自然行为将创建更好的、可用于发展人群控制策略的模型。


Emergency response teams such as policemen, the National Guard, military and even volunteers must undergo some type of crowd control training. Using researched principles of human behavior in crowds can give disaster training designers more elements to incorporate to create realistic simulated disasters. Crowd behavior can be observed during panic and non-panic conditions. When natural and unnatural events toss social ideals into a twisting chaotic bind, such as the events of 9/11 and hurricane Katrina, humanity's social capabilities are truly put to the test. Military programs are looking more towards simulated training, involving emergency responses, due to their cost-effective technology as well as how effective the learning can be transferred to the real world.[citation needed] Many events that may start out controlled can have a twisting event that turns them into catastrophic situations, where decisions need to be made on the spot. It is these situations in which crowd dynamical understanding would play a vital role in reducing the potential for anarchy.

Emergency response teams such as policemen, the National Guard, military and even volunteers must undergo some type of crowd control training. Using researched principles of human behavior in crowds can give disaster training designers more elements to incorporate to create realistic simulated disasters. Crowd behavior can be observed during panic and non-panic conditions. When natural and unnatural events toss social ideals into a twisting chaotic bind, such as the events of 9/11 and hurricane Katrina, humanity's social capabilities are truly put to the test. Military programs are looking more towards simulated training, involving emergency responses, due to their cost-effective technology as well as how effective the learning can be transferred to the real world. Many events that may start out controlled can have a twisting event that turns them into catastrophic situations, where decisions need to be made on the spot. It is these situations in which crowd dynamical understanding would play a vital role in reducing the potential for anarchy.

警察、国民警卫队、军队甚至志愿者等紧急反应小组都必须接受某种形式的人群控制培训。运用研究出的群体中人类的行为原则,灾难训练设计者可以有更多元素来创造更逼真的模拟灾难。在恐慌和非恐慌情况下可以观察到群体行为。当自然和非自然的事件使社会理想状况陷入扭曲混乱的局面,比如“911”和飓风卡特里娜事件时,人类的社会能力就在真正地接受考验。军事项目更倾向于以模拟形式进行训练和应急反应,因为它们是成本低效益高的形式,并且能够有效地将所学转移到现实世界。许多事件一开始可能受到控制,但后来反转发展成为灾难性情况,因此需要当场做出决定。正是在这些情况下,对人群的动态理解将发挥重要作用,以减少出现混乱的可能性。


Modeling techniques of crowds vary from holistic or network approaches to understanding individualistic or behavioral aspects of each agent. For example, the Social Force Model describes a need for individuals to find a balance between social interaction and physical interaction. An approach that incorporates both aspects, and is able to adapt depending on the situation, would better describe natural human behavior, always incorporating some measure of unpredictability. With the use of multi-agent models understanding these complex behaviors has become a much more comprehensible task. With the use of this type of software, systems can now be tested under extreme conditions, and simulate conditions over long periods of time in the matter of seconds.

Modeling techniques of crowds vary from holistic or network approaches to understanding individualistic or behavioral aspects of each agent. For example, the Social Force Model describes a need for individuals to find a balance between social interaction and physical interaction. An approach that incorporates both aspects, and is able to adapt depending on the situation, would better describe natural human behavior, always incorporating some measure of unpredictability. With the use of multi-agent models understanding these complex behaviors has become a much more comprehensible task. With the use of this type of software, systems can now be tested under extreme conditions, and simulate conditions over long periods of time in the matter of seconds.

群体的建模技术从整体或网络到理解每个主体的个人或行为方面都各不相同。例如,社会力模型 Social Force Model描述了个体在社会互动和物理互动之间寻求平衡的需求。一种结合了这两个方面,并且能够根据具体情境进行调整的方法,将更好地描述自然的人类行为,并总是包含一些对不可预测性的测量。随着多主体模型的使用,理解这些复杂的行为已经成为一个易懂的任务。利用这种类型的软件,现在研究人员可以在极端条件下测试系统,也可以在几秒钟内模拟长期的情况变化。


In some situations, the behavior of swarms of non-human animals can be used as an experimental model of crowd behavior. The panic behavior of ants when exposed to a repellent chemical in a confined space with limited exit routes has been found to have both similarities and differences to equivalent human behavior.[50][51]

In some situations, the behavior of swarms of non-human animals can be used as an experimental model of crowd behavior. The panic behavior of ants when exposed to a repellent chemical in a confined space with limited exit routes has been found to have both similarities and differences to equivalent human behavior.

在某些情况下,非人类动物群体的行为可以作为群体行为的一个实验模型。人们发现,蚂蚁在出口有限且封闭的空间里暴露于令人反感的化学物质中时产生的惊慌行为,与同样情况下的人类行为既有相似之处,也有不同之处。[52][53]


Modeling individual behaviors 个体行为建模

Helbing proposed a model based on physics using a particle system and socio-psychological forces in order to describe human crowd behavior in panic situation, this is now called the Helbing Model. His work is based on how the average person would react in a certain situation. Although this is a good model, there are always different types of people present in the crowd and they each have their own individual characteristics as well as how they act in a group structure. For instance, one person may not react to a panic situation, while another may stops walking and interfere in the crowd dynamics as a whole. Furthermore, depending on the group structure, the individual action can change because the agent is part of a group, for example, returning to a dangerous place in order to rescue a member of that group. Helbing's model can be generalized incorporating individualism, as proposed by Braun, Musse, Oliveira and Bodmann.[54]

Helbing proposed a model based on physics using a particle system and socio-psychological forces in order to describe human crowd behavior in panic situation, this is now called the Helbing Model. His work is based on how the average person would react in a certain situation. Although this is a good model, there are always different types of people present in the crowd and they each have their own individual characteristics as well as how they act in a group structure. For instance, one person may not react to a panic situation, while another may stops walking and interfere in the crowd dynamics as a whole. Furthermore, depending on the group structure, the individual action can change because the agent is part of a group, for example, returning to a dangerous place in order to rescue a member of that group. Helbing's model can be generalized incorporating individualism, as proposed by Braun, Musse, Oliveira and Bodmann.

赫尔本 Helbing 提出了一个基于物理学的模型,使用粒子系统和社会心理力量来描述人群在恐慌情况下的行为,这个模型现在被称为 赫尔本模型 Helbing Model。他的工作基于在特定情况下普通人如何反应。虽然这是一个很好的模式,但是在人群中总是有不同类型的人,他们每个人都有自己的个人特点和在群体结构中的行为方式。例如,一个人可能不会对恐慌的情况做出反应,而另一个人可能会停下脚步并干扰整个人群的运动。此外,根据群体结构的不同,作为群体的一部分主体的个人行为可能会发生变化。例如,为了拯救该群体的一个成员而返回到一个危险的地方。如布劳恩 Braun, Musse,奥利韦拉 Oliveira和布罗德曼 Bodmann 提出的那样,赫尔本的模型可以概括为融入个人主义。[54]


In order to tackle this problem, individuality should be assigned to each agent, allowing to deal with different types of behaviors. Another aspect to tackle this problem is the possibility to group people, forming these group causes people to change their behavior as a function of part of the group structure. Each agent (individual) can be defined according to the following parameters:

In order to tackle this problem, individuality should be assigned to each agent, allowing to deal with different types of behaviors. Another aspect to tackle this problem is the possibility to group people, forming these group causes people to change their behavior as a function of part of the group structure. Each agent (individual) can be defined according to the following parameters:

为了解决这个问题,应该给每个主体分配个性,从而允许模型处理不同类型的行为。解决这个问题的另一个方法是把人们分组,形成这样的群体会使人们作为群体结构的一部分而改变他们的行为。

--嘉树讨论)我觉得原文Another aspect……到forming these group……再到 part of the group structure,应该是两句,是从forming处分开的两句话)

--WildBoar讨论)我觉得原文和翻译都没有问题


每个主体(个体)可以根据以下参数定义:


  1. Id – Agent identifier
Id – Agent identifier

Id:主体标识符

  1. IdFamily – Identifier of the family. A family is a predefined group formed by agents who know each other
IdFamily – Identifier of the family. A family is a predefined group formed by agents who know each other

IdFamily:家族标识符。家族是由相互认识的主体组成的一个预先设定的组。

  1. DE – Dependence level of the agent which mimics the need for help. Values [0,1]
DE – Dependence level of the agent which mimics the need for help. Values [0,1]

DE:主体的依赖水平 Dependence level,用来模拟主体对他人帮助的需求。取值范围:[0,1]

  1. AL – Altruism level representing the tendency to help other agents. Values [0,1]
AL – Altruism level representing the tendency to help other agents. Values [0,1]

AL:利他主义水平 Altruism Level,代表了帮助他人的倾向。取值范围:[0,1]

  1. vi – Speed of the agent
vi – Speed of the agent

vi :主体的速度


To model the effect of the dependence parameter with individual agents, the equation is defined as:

To model the effect of the dependence parameter with individual agents, the equation is defined as:

为了模拟单个主体的因变量参数的效应方程定义为:


[math]\displaystyle{ v_i = (1-DE)v_{max} }[/math]
[math]\displaystyle{ v_i = (1-DE)v_{max} }[/math]

[math]\displaystyle{ v_i = (1-DE)v_{max} }[/math]


When evaluating the speed of the agent, it is clear that if the value of the dependence factor, DE, is equal to one, then the person would be fully disabled making him unable to move. If the dependence factor is equal to zero, then the person is able to run at his max speed.

When evaluating the speed of the agent, it is clear that if the value of the dependence factor, DE, is equal to one, then the person would be fully disabled making him unable to move. If the dependence factor is equal to zero, then the person is able to run at his max speed.

在评估主体的速度时,很明显,如果依赖因子 DE 的值等于1,则表示该主体完全丧失行动能力。如果依赖因子等于0,那么该主体能够以其最大速度运动。


Group formation is related to the Altruism force which is implemented as an interaction force between two or more agents who are part of the same family. Mathematically, it is described as the following:

Group formation is related to the Altruism force which is implemented as an interaction force between two or more agents who are part of the same family. Mathematically, it is described as the following:

群体的形成与利他主义力有关。利他主义力是同一家族中两个或两个以上成员之间的相互作用力。用数学方法,它被描述为:


[math]\displaystyle{ F\overline{a}_i = K\sum \left ( AL_iDE_j \times \left | d_{ij}-d_{ip} \right | \times e_{ij} \right ) }[/math]
[math]\displaystyle{ F\overline{a}_i = K\sum \left ( AL_iDE_j \times \left | d_{ij}-d_{ip} \right | \times e_{ij} \right )  }[/math]

[math]\displaystyle{ F\overline{a}_i = K\sum \left ( AL_iDE_j \times \left | d_{ij}-d_{ip} \right | \times e_{ij} \right ) }[/math]


where:

where:

其中:

dij represents the distance between two agents with the origin at the position of the agent;
dij represents the distance between two agents with the origin at the position of the agent;

dij表示两个主体之间的距离,原点设在主体的位置;

dip is the distance vector point from the agents to the door's position p of the simulation environment;
dip is the distance vector point from the agents to the door's position of the simulation environment;

dip是模拟环境中从主体到门的位置的距离矢量点;

K is a constant;
K is a constant;

K是一个常数

eij is the unitary vector with the origin at position i.
eij is the unitary vector with the origin at position i.

eij是原点位于i处的酉向量。


Consequently, the greater the parameter ALi of agent i, the bigger will be i which points to the agent j and has the high level of DEj. When both agents are close enough to each other, the one with high DE (agent j in this example) adopts the value of agent i ([math]\displaystyle{ DE_j = DE_i }[/math]). This means that the evacuation ability of agent i is shared with agent j and both start moving together.

Consequently, the greater the parameter of agent , the bigger will be which points to the agent and has the high level of . When both agents are close enough to each other, the one with high (agent in this example) adopts the value of agent ([math]\displaystyle{ DE_j = DE_i }[/math]). This means that the evacuation ability of agent is shared with agent and both start moving together.

因此,主体i的利他主义水平ALi越高,指向主体ji就越大,j也具有较高的依赖水平DEj。当两个代理彼此足够接近时,具有较高依赖水平的主体(本例中的主体j)就采用主体i依赖水平([math]\displaystyle{ DE_j = DE_i }[/math])。这意味着主体i与主体j共享疏散能力,两者一起开始移动。


By using these applying these equations in model testing using a normally distributed population, the results are fairly similar to the Helbing Model.

By using these applying these equations in model testing using a normally distributed population, the results are fairly similar to the Helbing Model.

将这些方程应用于正态分布人口的模型检验,结果与赫尔本模型非常相似。

--WildBoar讨论)这句前半句英文的结构我没太懂,中文的翻译也不知道是否准确

--Vicky讨论)By using these (applying these) equations in model testing (using a normally distributed population)


The places where this would be helpful would be in an evacuation scenario. Take for example, an evacuation of a building in the case of a fire. Taking into account the characteristics of individual agents and their group performances, determining the outcome of how the crowd would exit the building is critically important in creating the layout of the building.[54]

The places where this would be helpful would be in an evacuation scenario. Take for example, an evacuation of a building in the case of a fire. Taking into account the characteristics of individual agents and their group performances, determining the outcome of how the crowd would exit the building is critically important in creating the layout of the building.

这种模型会对疏散场景有所帮助。以发生火灾的建筑物疏散为例,考虑到个体的特点和他们在团队的表现从而确定人群会如何离开建筑的结果,对于设计建筑的布局至关重要。[54]

Leader behavior during evacuation simulations 疏散模拟中的领导者行为

As described earlier, the Helbing Model is used as the basics for crowd behavior. This same type of behavior model is used for evacuation simulations.

As described earlier, the Helbing Model is used as the basics for crowd behavior. This same type of behavior model is used for evacuation simulations.

如前所述,赫尔本模型被用作群体行为的基础。同样类型的行为模型也同样被用于疏散模拟。


In general, the first thing that has to be assumed is that not everyone has knowledge about the environment or where there are and aren't hazards. From this assumption we can create three types of agents. The first type is a trained leader, this agent knows about the environment and is able to spread knowledge to other agents so they know how to exit from an environment. The next type of agent is an untrained leader, this agent does not know about the environment, however, as the agent explores the environment and gets information from other types of leaders, the agent is able to spread the knowledge about the environment. The last type of agent is a follower, this type of agent can only take information from other leaders and not be able to share the information with other agents.

In general, the first thing that has to be assumed is that not everyone has knowledge about the environment or where there are and aren't hazards. From this assumption we can create three types of agents. The first type is a trained leader, this agent knows about the environment and is able to spread knowledge to other agents so they know how to exit from an environment. The next type of agent is an untrained leader, this agent does not know about the environment, however, as the agent explores the environment and gets information from other types of leaders, the agent is able to spread the knowledge about the environment. The last type of agent is a follower, this type of agent can only take information from other leaders and not be able to share the information with other agents.

一般来说,首先需要假定的是,并不是每个人都了解环境或了解哪里有没有危险。根据这个假设,我们可以创建三种类型的主体。第一种类型是训练有素的领导者,这种主体了解环境并能够将知识传播给其他主体,因此他们知道如何从这种环境中退出。第二种类型的主体是未经训练的领导者。他们不了解环境,但是当这种主体探索环境并从其他类型的领导者那里获得信息时,他们能够传播有关环境的知识。最后一种类型是跟随者,这种类型的主体只能从其他领导者那里获取信息,不能与其他主体共享信息。


The implementation of these types of agents is fairly straightforward. The leaders in the environment have a map of the environment saved as one of their attributes. An untrained leader and followers will start out with an empty map as their attribute. Untrained leaders and followers will start exploring an environment by themselves and create a map of walkable and unwalkable locations. Leaders and untrained leaders (once they have the knowledge), will share information with other agents depending on their proximity. They will share information about which points on the grid are blocked, the local sub-graphs and the dangers in the area.

The implementation of these types of agents is fairly straightforward. The leaders in the environment have a map of the environment saved as one of their attributes. An untrained leader and followers will start out with an empty map as their attribute. Untrained leaders and followers will start exploring an environment by themselves and create a map of walkable and unwalkable locations. Leaders and untrained leaders (once they have the knowledge), will share information with other agents depending on their proximity. They will share information about which points on the grid are blocked, the local sub-graphs and the dangers in the area.

实现这些主体类型的方法相当简单直接。对于领导者,环境地图被保存为他们的属性之一。未经训练的领导者和追随者具有一张空地图的属性。未经训练的领导者和追随者将自己开始探索环境,并创建一个具有适合和不适合走地点信息的地图。领导者和未受过训练的领导者(一旦他们掌握了知识) ,会根据与其他主体的接近程度来分享信息。他们分享的信息包括网格上哪些点被阻塞,局部子地图和该地区的危险。


There were two types of searching algorithms tried out for this implementation. There was the random search and the depth first search. A random search is where each of the agents go in any direction through the environment and try to find a pathway out. The depth first search is where agents follow one path as far as it can go then return and try another path if the path they traversed does not contain an exit. If was found that depth first search gave a speed up of 15 times versus a random search.[55]

There were two types of searching algorithms tried out for this implementation. There was the random search and the depth first search. A random search is where each of the agents go in any direction through the environment and try to find a pathway out. The depth first search is where agents follow one path as far as it can go then return and try another path if the path they traversed does not contain an exit. If was found that depth first search gave a speed up of 15 times versus a random search.

有两种类型的搜索算法试验了这种模型: 随机搜索 Random Search深度优先搜索 Depth First Search。随机搜索是指每个主体在环境中朝任意方向前进,并试图找到一条出路。深度优先搜索是指主体尽可能沿着一条路径搜索,如果它们所经过的路径不包含出口则返回并尝试另一条路径。结果发现,深度优先搜索比随机搜索的速度快15倍。


Scalable simulations 可伸缩的模拟

--嘉树讨论) Scalable 翻译为“可变的”会不会好一点? 此处的搭配包括Scalable Agents,Scalable architecture 和Scalable simulations

--WildBoar讨论) Scalable 好像没有“可变的”含义,可以是“可扩展的”

--Vicky讨论) 可伸缩模拟 或者 可扩展模拟 都可以

There are many different case situations that come into play in crowd simulations.[56] Recently, crowd simulation has been essential for the many virtual environment applications such as education, training, and entertainment. Many situations are based on the environment of the simulation or the behavior of the group of local agents. In virtual reality applications, every agent interacts with many other agents in the environment, calling for complex real-time interactions. Agents must have continuous changes in the environment since agent behaviors allow complex interactions. Scalable architecture can manage large crowds through the behavior and interactive rates. These situations will indicate how the crowds will act in multiple complex scenarios while several different situations are being applied. A situation can be any circumstance that has typical local behaviors. We can categorize all situations into two different kinds.

There are many different case situations that come into play in crowd simulations. Recently, crowd simulation has been essential for the many virtual environment applications such as education, training, and entertainment. Many situations are based on the environment of the simulation or the behavior of the group of local agents. In virtual reality applications, every agent interacts with many other agents in the environment, calling for complex real-time interactions. Agents must have continuous changes in the environment since agent behaviors allow complex interactions. Scalable architecture can manage large crowds through the behavior and interactive rates. These situations will indicate how the crowds will act in multiple complex scenarios while several different situations are being applied. A situation can be any circumstance that has typical local behaviors. We can categorize all situations into two different kinds.

在人群模拟中有许多不同的情况出现。[57]近年来,人群模拟已经成为教育、训练、娱乐等虚拟环境应用中不可缺少的一部分。许多情况下的人群模拟是基于模拟的环境或一组局部主体的行为。在虚拟现实应用程序中,每个主体与环境中的许多其他主体进行交互,需要复杂的实时交互。由于主体行为允许复杂交互的特性,主体必须在环境中能够连续变化。可伸缩的架构 Scalable architecture可以通过行为和交互速率来管理大的人群。当应用几种不同的情境时,可以揭示人群在复杂的情况下的运动行为。情境可以是任何具有典型局部行为的环境。

--WildBoar讨论) 这两句说不通,situation到底指什么?

--Vick讨论) 英语表达有些重复,可以省略第一个situation的翻译


我们可以把所有的情境分为两种不同的类型。


Spatial situation is a situation that has a region where the environment affects the local agents. For instance, a crowd waiting in line for a ticket booth would be displaying a spatial situation. Other examples may be a bus stop or an ATM where characters act upon their environment. Therefore, we would consider 'bus stop' as the situation if the behavior of the agents are to be getting on or off a bus.

Spatial situation is a situation that has a region where the environment affects the local agents. For instance, a crowd waiting in line for a ticket booth would be displaying a spatial situation. Other examples may be a bus stop or an ATM where characters act upon their environment. Therefore, we would consider 'bus stop' as the situation if the behavior of the agents are to be getting on or off a bus.

空间情境 Spatial Situation是指环境影响局部主体的区域。例如,一个售票亭前排队等候的人群就展示可一个空间情境。其他的例子,可能是一个公交车总站或者一个ATM机,在那里人依据其环境做出行动。因此,如果主体的行为可以是上车或下车,我们就可以把“公交车站”看作是一种空间情境。


Non-Spatial situation has no region in the environment because this only involves the behavior of the crowd. The relationship of the local agents is an important factor to consider when determining behavior. An example would be a group of friends walking together. Typical behavior of characters that are friends would all move along with each other. This means that 'friendship' would be the situation among the typical behavior of walking together.

Non-Spatial situation has no region in the environment because this only involves the behavior of the crowd. The relationship of the local agents is an important factor to consider when determining behavior. An example would be a group of friends walking together. Typical behavior of characters that are friends would all move along with each other. This means that 'friendship' would be the situation among the typical behavior of walking together.

非空间情境 Non-Spatial situation是没有空间区域的环境,因为它只涉及到人群的行为。在决定行为时,局部主体之间的关系是一个重要的因素。一个例子是一群朋友一起散步。朋友的典型行为是各个主体都会互相影响。这意味着“友谊”是作为一起散步的典型行为的情境。


The structure of any situation is built upon four components, Behavior functions, Sensors, States, and Event Rules. Behavior functions represent what the characters behaviors are specific to the situation. Sensors are the sensing capability for agents to see and respond to events. States are the different motions and state transitions used only for the local behaviors. Event rule is the way to connect different events to their specific behaviors. While a character is being put into a situation, these four components are considered at the same time. For spatial situations, the components are added when the individual initially enters the environment that influences the character. For non-spatial situations, the character is affected only once the user assigns the situation to the character. The four components are removed when the agent is taken away from its situations region or the situation itself is removed. The dynamic adding and removing of the situations lets us achieve scalable agents.

The structure of any situation is built upon four components, Behavior functions, Sensors, States, and Event Rules. Behavior functions represent what the characters behaviors are specific to the situation. Sensors are the sensing capability for agents to see and respond to events. States are the different motions and state transitions used only for the local behaviors. Event rule is the way to connect different events to their specific behaviors. While a character is being put into a situation, these four components are considered at the same time. For spatial situations, the components are added when the individual initially enters the environment that influences the character. For non-spatial situations, the character is affected only once the user assigns the situation to the character. The four components are removed when the agent is taken away from its situations region or the situation itself is removed. The dynamic adding and removing of the situations lets us achieve scalable agents.

任何情境的结构都建立在四个组件之上: 行为函数 Behavior Functions传感器 Sensors状态 States事件规则 Event Rules。行为函数表示特定情况下的特定行为。传感器是一种感知能力,可以让主体看到并对事件做出反应。状态是仅用于局部行为的不同运动和状态转移。事件规则是将不同的事件与其特定的行为联系起来的方法。当一个角色被置于一个情境中时,这四个组件将被同时考虑。对于空间情景,当个体最初进入影响角色的环境时就添加组件。对于非空间情境,只有当用户将情境赋给角色时,角色才会受到影响。当主体被带离情境区域或情境本身被移除时,这四个组件被移除。动态地添加和删除这些情境使我们能够实现可伸缩的主体 Scalable Agents

Human-like behaviors and crowd AI 类人行为和群体人工智能

文件:Crowd simulation, Covent Garden.jpg
A crowd simulation of Covent Garden square, London, showing a crowd of pedestrian agents reacting to a street performer

A crowd simulation of Covent Garden square, London, showing a crowd of pedestrian agents reacting to a street performer

一个人群模拟Covent Garden 广场,伦敦,显示了一群行人主体对街头艺人的反应

To simulate more aspects of human activities in a crowd, more is needed than path and motion planning. Complex social interactions, smart object manipulation, and hybrid models are challenges in this area.Simulated crowd behavior is inspired by the flow of real-world crowds. Behavioral patterns, movement speeds and densities, and anomalies are analyzed across many environments and building types. Individuals are tracked and their movements are documented such that algorithms can be derived and implemented into crowd simulations.

To simulate more aspects of human activities in a crowd, more is needed than path and motion planning. Complex social interactions, smart object manipulation, and hybrid models are challenges in this area.Simulated crowd behavior is inspired by the flow of real-world crowds. Behavioral patterns, movement speeds and densities, and anomalies are analyzed across many environments and building types. Individuals are tracked and their movements are documented such that algorithms can be derived and implemented into crowd simulations.

为了模拟在人群中人类活动的更多方面,我们需要路径和运动规划以外的更多东西。复杂的社会交互、智能对象操纵和混合模型是这一领域的挑战。模拟人群行为的灵感来自于现实世界中的人群流。研究者分析了人群在许多环境和建筑类型中的行为模式、运动速度和密度以及异常情况。个体的行动被跟踪记录下来,这样就可以得到算法并应用到人群模拟中。


Individual entities in a crowd are also called agents. In order for a crowd to behave realistically each agent should act autonomously (be capable of acting independently of the other agents). This idea is referred to as an agent-based model. Moreover, it is usually desired that the agents act with some degree of intelligence (i.e. the agents should not perform actions that would cause them to harm themselves). For agents to make intelligent and realistic decisions, they should act in accordance with their surrounding environment, react to its changes, and react to the other agents.

Individual entities in a crowd are also called agents. In order for a crowd to behave realistically each agent should act autonomously (be capable of acting independently of the other agents). This idea is referred to as an agent-based model. Moreover, it is usually desired that the agents act with some degree of intelligence (i.e. the agents should not perform actions that would cause them to harm themselves). For agents to make intelligent and realistic decisions, they should act in accordance with their surrounding environment, react to its changes, and react to the other agents.

群体中的独立的个体也称为主体。为了让一个群体真实地行动,每个主体应该自主地(能够独立于其他主体地)行动。这个想法被称为基于主体的模型 Agent-based Model。此外,人们通常希望主体的行为具有一定程度的智慧(即,主体不应采取伤害自己的行动)。为了使主体做出明智和真实的决定,他们应该根据他们周围的环境采取行动,对环境变化作出反应,并对其他主体作出反应。


Rule-based AI 基于规则的人工智能

文件:MaslowsHierarchyOfNeeds.svg
Maslow's Hierarchy of Needs

Maslow's Hierarchy of Needs

马斯洛的需求层次理论

In rule-based AI, virtual agents follow scripts: "if this happens, do that". This is a good approach to take if agents with different roles are required, such as a main character and several background characters. This type of AI is usually implemented with a hierarchy, such as in Maslow's hierarchy of needs, where the lower the need lies in the hierarchy, the stronger it is.

In rule-based AI, virtual agents follow scripts: "if this happens, do that". This is a good approach to take if agents with different roles are required, such as a main character and several background characters. This type of AI is usually implemented with a hierarchy, such as in Maslow's hierarchy of needs, where the lower the need lies in the hierarchy, the stronger it is.

基于规则的人工智能 Rule-based AI中,虚拟主体遵循以下规则: “如果发生了这种情况,就那样做”。如果需要具有不同角色的主体,比如一个主角和几个背景角色,那么这是一个很好的方法。这种类型的人工智能通常是用层次结构来实现的,例如在马斯洛的需求层次结构 Maslow's hierarchy of needs中,需求层级越低,需求就越强。


For example, consider a student walking to class who encounters an explosion and runs away. The theory behind this is initially the first four levels of his needs are met, and the student is acting according to his need for self-actualization. When the explosion happens his safety is threatened which is a much stronger need, causing him to act according to that need.

For example, consider a student walking to class who encounters an explosion and runs away. The theory behind this is initially the first four levels of his needs are met, and the student is acting according to his need for self-actualization. When the explosion happens his safety is threatened which is a much stronger need, causing him to act according to that need.

例如,假设一个学生在走向教室的路上遭遇了爆炸并逃跑了。这背后的理论是他的前4个层次的需求得到了满足,并且学生是具有自我实现 self-actualization的需求并依次按照需求来行动。当爆炸发生时,他的安全受到威胁,这是一个比自我实现更强烈的需要,使他按照这种需要行事。


This approach is scalable, and can be applied to crowds with a large number of agents. Rule-based AI, however, does have some drawbacks. Most notably the behavior of the agents can become very predictable, which may cause a crowd to behave unrealistically.

This approach is scalable, and can be applied to crowds with a large number of agents. Rule-based AI, however, does have some drawbacks. Most notably the behavior of the agents can become very predictable, which may cause a crowd to behave unrealistically.

这种方法是可扩展的,可以应用于拥有大量主体的人群。然而,基于规则的人工智能确实有一些缺点。最值得注意的缺点是,主体的行为会变得可预测性很强,这可能导致群体的行为不现实。


Learning AI 学习型人工智能

In learning AI, virtual characters behave in ways that have been tested to help them achieve their goals. Agents experiment with their environment or a sample environment which is similar to their real one.

In learning AI, virtual characters behave in ways that have been tested to help them achieve their goals. Agents experiment with their environment or a sample environment which is similar to their real one.

学习型人工智能 Learning AI中,虚拟角色用一种经过了测试的方式帮助他们自己实现目标。主体用他们的环境或者一个类似于他们真实环境的样本环境进行测试。


Agents perform a variety of actions and learn from their mistakes. Each agent alters its behavior in response to rewards and punishments it receives from the environment. Over time, each agent would develop behaviors that are consistently more likely to earn high rewards.

Agents perform a variety of actions and learn from their mistakes. Each agent alters its behavior in response to rewards and punishments it receives from the environment. Over time, each agent would develop behaviors that are consistently more likely to earn high rewards.

主体进行各种各样的行动并从他们的错误中吸取经验。每个主体根据从环境中得到的奖励和惩罚改变其行为。随着时间的推移,每个主体都会发展更有可能获得高回报的行为。


If this approach is used, along with a large number of possible behaviors and a complex environment agents will act in a realistic and unpredictable fashion.

If this approach is used, along with a large number of possible behaviors and a complex environment agents will act in a realistic and unpredictable fashion.

如果使用这种方法,加上大量可能的行为和复杂的环境,主体将以一种现实的和不可预测的方式行事。


Algorithms 算法

There are a wide variety of machine learning algorithms that can be applied to crowd simulations.

There are a wide variety of machine learning algorithms that can be applied to crowd simulations.

人群模拟现在已经有各种各样可以应用的机器学习算法。


Q-Learning is an algorithm residing under machine learning's sub field known as reinforcement learning. A basic overview of the algorithm is that each action is assigned a Q value and each agent is given the directive to always perform the action with the highest Q value. In this case learning applies to the way in which Q values are assigned, which is entirely reward based. When an agent comes in contact with a state, s, and action, a, the algorithm then estimates the total reward value that an agent would receive for performing that state action pair. After calculating this data, it is then stored in the agent's knowledge and the agent proceeds to act from there.

Q-Learning is an algorithm residing under machine learning's sub field known as reinforcement learning. A basic overview of the algorithm is that each action is assigned a Q value and each agent is given the directive to always perform the action with the highest Q value. In this case learning applies to the way in which Q values are assigned, which is entirely reward based. When an agent comes in contact with a state, s, and action, a, the algorithm then estimates the total reward value that an agent would receive for performing that state action pair. After calculating this data, it is then stored in the agent's knowledge and the agent proceeds to act from there.

Q-Learning是机器学习的子领域强化学习中的一个算法。一个对该算法的基本描述:每个动作都被分配了一个Q值,每个主体都被赋予一个总是执行Q值最高的动作的指令。在这种情况下,学习效果取决于分配Q值的方式,这完全是基于奖励的。当一个主体接触到一个状态 s 和一个动作 a 时,算法就会估计这个主体执行这个状态动作对 state action pair所能得到的总回报值。在计算这些数据之后,它们被存储在主体的知识中,主体依据这些知识开始行动。

(--嘉树讨论) learning applies to the way in which Q values are assigned 翻译为学习效果取决于分配 q 值的方式,不知是否正确)

(--WildBoar讨论)我觉得应该不是学习效果,应该是学习的内容)


The agent will constantly alter its behavior depending on the best Q value available to it. And as it explores more and more of the environment, it will eventually learn the most optimal state action pairs to perform in almost every situation.

The agent will constantly alter its behavior depending on the best Q value available to it. And as it explores more and more of the environment, it will eventually learn the most optimal state action pairs to perform in almost every situation.

主体会根据最佳Q值不断地改变自己的行为。随着它对环境的探索越来越多,它最终将学习到几乎每种情况下最佳状态动作对。


The following function outlines the bulk of the algorithm:

The following function outlines the bulk of the algorithm:

下面的函数概括了算法的大部分内容:


Q(s, a) ←− r + maxaQ(s', a')

Q(s, a) ←− r + maxaQ(s', a')

Q(s, a) ←− r + maxaQ(s', a')


Given a state s and action a, r and s’ are the reward and state after performing (s,a), and a' is the range over all the actions.[58]

Given a state s and action a, r and s’ are the reward and state after performing (s,a), and a' is the range over all the actions.

给定一个状态s和动作a,r和s’是执行(s,a)后的奖励和状态,a'动作集。[59]

--WildBoar讨论)r和s这句,我觉得s应该是s’吧

--Vicky讨论)同意S'

Crowd rendering and animation 人群渲染和动画

Rendering and animating a large number of agents realistically, especially in real time, is challenging. To reduce the complexity of 3D rendering of large-scale crowds, techniques like culling (discarding unimportant objects), impostors(image-based rendering) and decreasing levels of detail have been used.[60]

Rendering and animating a large number of agents realistically, especially in real time, is challenging. To reduce the complexity of 3D rendering of large-scale crowds, techniques like culling (discarding unimportant objects), impostors(image-based rendering) and decreasing levels of detail have been used.

对大量主体进行真实地、特别是实时地渲染和动画绘制,是具有挑战性的。为了降低大规模人群3D渲染的复杂性,人们使用了剔除(舍弃不重要的物体)、顶替(基于图像的渲染)和降低细节的级别等技术。[61]


Variations in appearance, body shape and size, accessories and behavior(social or cultural) exist in real crowds, and lack of variety affects the realism of visual simulations. Existing systems can create virtual crowds with varying texture,[11] color,[62] size, shape and animation.[10]

Variations in appearance, body shape and size, accessories and behavior(social or cultural) exist in real crowds, and lack of variety affects the realism of visual simulations. Existing systems can create virtual crowds with varying texture, size, shape and animation.

真实的人群中存在着外貌、体型大小、附件和(社会的或文化的)行为的差异,缺乏多样性的模型会影响视觉模拟的真实性。现有的系统可以创建具有不同肌理、[11] 颜色、[63] 大小、形状和动画的虚拟人群。[10]

(--嘉树讨论) 最后一个animation译为运动画面了,译为“动画”呢?存疑)

(--WildBoar讨论) 动画吧)


Real world applications 在真实世界的应用

Virtual cinematography 虚拟电影摄影

Crowd simulations have been used widely across films as a cost-effective and realistic alternative from hiring actors and capturing shots that would otherwise be unrealistic. A significant example of its use lies in The Lord of the Rings (film series). One of the most glaring problems for the production team in the initial stages were large-scale battles, as the author of the novels, J. R. R. Tolkien, envisioned them to have at least 50,000 participants. Such a number was unrealistic had they decided to only attempt to hire real actors and actresses. Instead they decided to use CG to simulate these scenes through the use of the Multiple Agent Simulation System in a Virtual Environment, otherwise known as MASSIVE. The Human Logic Engine based Maya plugin for crowd simulation, Miarmy, was used for the development of these sequences. The software allowed the filmmakers to provide each character model an agent based A.I. that could utilize a library of 350 animations. Based on sight, hearing, and touch parameters generated from the simulation, agents would react uniquely to each situation. Thus each simulation of the scene was unpredictable. The final product clearly displayed the advantages to using crowd simulation software.[64]


Crowd simulations have been used widely across films as a cost-effective and realistic alternative from hiring actors and capturing shots that would otherwise be unrealistic. A significant example of its use lies in The Lord of the Rings (film series). One of the most glaring problems for the production team in the initial stages were large-scale battles, as the author of the novels, J. R. R. Tolkien, envisioned them to have at least 50,000 participants. Such a number was unrealistic had they decided to only attempt to hire real actors and actresses. Instead they decided to use CG to simulate these scenes through the use of the Multiple Agent Simulation System in a Virtual Environment, otherwise known as MASSIVE. The Human Logic Engine based Maya plugin for crowd simulation, Miarmy, was used for the development of these sequences. The software allowed the filmmakers to provide each character model an agent based A.I. that could utilize a library of 350 animations. Based on sight, hearing, and touch parameters generated from the simulation, agents would react uniquely to each situation. Thus each simulation of the scene was unpredictable. The final product clearly displayed the advantages to using crowd simulation software.

人群模拟已经被广泛应用于电影中,它是一种相较雇佣演员和捕捉镜头来说更加低成本高收益且现实的替代方式,使用其他方法将是不真实的。电影《指环王》系列就是一个重要的例子。最初,制作团队面临最突出的问题之一是大规模的战斗,因为小说的作者约翰·罗纳德·瑞尔·托尔金 J. R. R. Tolkien设想这些战斗至少有50,000名参与者。如果制作团队决定只雇佣真人演员,这么多参与者是不现实的。他们决定使用CG来代替模拟这些场景,使用虚拟环境中的多主体仿真系统 Multiple Agent Simulation System,也就是众所周知的 MASSIVE 。群集动画 Miarmy,一个基于人类逻辑引擎 Human Logic Engine的人群模拟 Maya 插件,被用于开发这些动作序列。该软件允许制片人为每个角色提供一个基于主体的人工智能模型,这个模型可以利用一个有350个动画的数据库。基于通过模拟产生的视觉、听觉和触觉参数,主体将对每种不同的情况做出特定反应。因此,每个场景的模拟都是不可预知的。最终作品清晰地展示了使用人群模拟软件的优势。[65]



Urban planning 城市规划

The development of crowd simulation software has become a modern and useful tool in designing urban environments. Whereas the traditional method of urban planning relies on maps and abstract sketches, a digital simulation is more capable of conveying both form and intent of design from architect to pedestrian. For example, street signs and traffic lights are localized visual cues that influence pedestrians to move and behave accordingly. Following this logic, a person is able to move from point A to point B in a way that is efficient and that a collective group of people can operate more effectively as a result. In a broader sense, bus systems and roadside restaurants serve a spatial purpose in their locations through an understanding of human movement patterns.[66] The SimCity video game series exemplifies this concept in a more simplistic manner. In this series, the player assigns city development in designated zones while maintaining a healthy budget. The progression from empty land to a bustling city is fully controlled by the player's choices and the digital citizens behave as according to the city's design and events.

The development of crowd simulation software has become a modern and useful tool in designing urban environments. Whereas the traditional method of urban planning relies on maps and abstract sketches, a digital simulation is more capable of conveying both form and intent of design from architect to pedestrian. For example, street signs and traffic lights are localized visual cues that influence pedestrians to move and behave accordingly. Following this logic, a person is able to move from point A to point B in a way that is efficient and that a collective group of people can operate more effectively as a result. In a broader sense, bus systems and roadside restaurants serve a spatial purpose in their locations through an understanding of human movement patterns. The SimCity video game series exemplifies this concept in a more simplistic manner. In this series, the player assigns city development in designated zones while maintaining a healthy budget. The progression from empty land to a bustling city is fully controlled by the player's choices and the digital citizens behave as according to the city's design and events.

人群模拟软件的发展为现代城市环境设计提供了一个有用的工具。传统的城市规划方法依赖于地图和抽象的草图,而数字仿真更能将设计的形式和意图从建筑设计师传达给行人。例如,街道标志和交通灯是局部视觉线索,能够影响行人的移动和相应的行为。按照这个逻辑,如果一个人能够以一种高效的方式从 a 点移动到 b 点,一群人就能够高效地运作。在更广泛的意义上,公交系统和路边餐馆通过对人类运动模式的理解,在其位置起到空间上的作用。[67]模拟城市SimCity视频游戏系列以一种更简单的方式举例说明了这个概念。在这个系列中,玩家在指定的区域指导城市的发展,同时保持平衡的收支。从空地到繁华城市的过程完全由玩家的选择来控制,而数字市民的行为则根据城市的设计和事件而定。


Evacuation and riot handling 疏散和暴乱处理

Simulated realistic crowds can be used in training for riots handling, architecture, safety science (evacuation planning).

Simulated realistic crowds can be used in training for riots handling, architecture, safety science (evacuation planning).

模拟真实的人群可用于训练暴乱处理、建筑、安全科学(疏散计划 Evacuation )。


Military 军队

Being that crowd simulations are so prevalent in use for public planning and general order with regards to chaotic situations, many applications can be drawn for governmental and military simulations. Crowd modeling is essential in police and military simulation in order to train officers and soldiers to deal with mass gatherings of people. Not only do offensive combatants prove to be difficult for these individuals to handle, but noncombatant crowds play significant roles in making these aggressive situations more out of control. Game technology is used in order to simulate these situations for soldiers and technicians to practice their skills.[68]

Being that crowd simulations are so prevalent in use for public planning and general order with regards to chaotic situations, many applications can be drawn for governmental and military simulations. Crowd modeling is essential in police and military simulation in order to train officers and soldiers to deal with mass gatherings of people. Not only do offensive combatants prove to be difficult for these individuals to handle, but noncombatant crowds play significant roles in making these aggressive situations more out of control. Game technology is used in order to simulate these situations for soldiers and technicians to practice their skills.

由于人群模拟是如此普遍地应用于公共规划和建立混乱情境中的一般秩序,许多应用程序可以用于绘制政府的和军队的模拟。为了训练官兵对群众集会的应对能力,人群模型在警察和军队推演中是必不可少的。在这些模拟中,不仅进攻性的战斗人员很难处理,非战斗人员也在使这些斗争场景更加失控的方面发挥了重要的作用。因此游戏技术被用来模拟这些情境,以使士兵和技术人员实践他们的技能。[69]


Sociology 社会学

The behavior of a modeled crowd plays a prominent role in analytical matters. These dynamics rely on the physical behaviors of individual agents within a crowd rather than the visual reality of the model itself. The social behaviors of people within these constructs have been of interest for many years, and the sociological concepts which underpin these interactions are constantly studied. The simulation of crowds in different situations allows for sociological study of real life gatherings in a variety of arrangements and locations. The variations in human behavior in situations varying in stress-levels allows for the further development and creation of crowd control strategies which can be more specifically applied to situations rather than generalized.

The behavior of a modeled crowd plays a prominent role in analytical matters. These dynamics rely on the physical behaviors of individual agents within a crowd rather than the visual reality of the model itself. The social behaviors of people within these constructs have been of interest for many years, and the sociological concepts which underpin these interactions are constantly studied. The simulation of crowds in different situations allows for sociological study of real life gatherings in a variety of arrangements and locations. The variations in human behavior in situations varying in stress-levels allows for the further development and creation of crowd control strategies which can be more specifically applied to situations rather than generalized.

模型群体的行为在分析问题时有重要的作用。这些动画依赖于独立主体在群体中的物理行为,而不是模型本身的视觉现实。多年来,人们在这些结构中的社会行为一直是研究者感兴趣的问题,支撑这些互动的社会学概念也被持续地研究。不同情境下的人群模拟使现实生活中不同场景地点集会的社会学研究得以进行。人类在压力水平不同的情境下的行为差异可以用来进一步发展和创造人群控制策略,这种策略可以更具体而不是空泛地应用于所有情况。


See also 参见

Crowd simulation software 人群模拟软件

References 参考文献

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External links 外部链接

! ——维基百科不是公司或产品的外部链接目录——

  • SteerSuite, An open-source framework for developing and evaluating crowd simulation algorithms


模板:Animation

模板:Collective animal behaviour

Category:Crowds

类别: Crowds

Category:Computer animation

类别: 电脑动画

Category:3D computer graphics

类别: 三维计算机图形

Category:Social physics

类别: 社会物理学


This page was moved from wikipedia:en:Crowd simulation. Its edit history can be viewed at 人群模拟/edithistory

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