Crowd simulation - Knowledge

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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.

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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.

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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 stop 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.

1807:. In this case, the focus is on the behavior of the crowd, not necessarily on 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, often in public safety planning. 1814:

be observed during both panic and non-panic conditions. 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 could play a vital role in reducing the potential for chaos.

2332: 2383: 2475:. 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 5584: 2288:

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.

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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

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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.

5380: 2483:, 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. 5390: 1821:

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.

456:, 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. 580:

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.

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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.

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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.

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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.

1699: 349:. 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 382:

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.

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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

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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

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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

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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.

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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

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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

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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

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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

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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.

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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.

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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

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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

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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,

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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

1841:(PNF), which was originally developed for robotics motion planning. The algorithm constructs a trajectory according to the probability for collision at each point in the entire crossing area. The pedestrian then follow a trajectory that locally minimizes their perceived probability for collision. 414:

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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

365:'s supervision of Soraia Raupp Musse's PhD thesis. They 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. 2457:

been used. 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, color, size, shape and animation.

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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

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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 or

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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.

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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.

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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.

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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:

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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.

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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.

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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.

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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.

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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:

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theory. 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.

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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.

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Hacohen, Shoval and Shvalb formulated the drivers-pedestrians dynamics at congested conflict spots. In such scenarios, the drivers and/or pedestrians do not closely follow the traffic laws. The model is based on the Probabilistic

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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

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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

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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.

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refers to stressors 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:

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Hacohen, S., Shvalb, N., & Shoval, S. (2018). Dynamic model for pedestrian crossing in congested traffic based on probabilistic navigation function. Transportation research part C: emerging technologies, 86,

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Matt Anderson; Eric McDaniel; Stephen Chenney (July 26–27, 2003). "Constrained animation of flocks". SCA '03 Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation. pp. 286–297.

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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

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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.

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McKenzie, F. D.; Petty, M. D.; Kruszewski, P. A.; Gaskins, R. C.; Nguyen, Q.-A. H.; Seevinck, J.; Weisel, E. W. (2007). "Integrating crowd-behavior modeling into military simulation using game technology".

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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.

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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:

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Pelechano, N. and Allbeck, J. M. and Badler, N. I. Controlling individual agents in high-density crowd simulation. In Proceedings of the 2007 ACM SIGGRAPH/Eurographics Symposium on Computer Animation.

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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.

1694:{\displaystyle {dS \over dt}={\begin{cases}\alpha &{\text{if }}\psi >S\\(-\alpha \leq {d\psi \over dt}\leq \alpha )&{\text{if }}\psi =S\\-\alpha &{\text{if }}\psi <S\end{cases}}} 1039: 1124:

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

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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.

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Gwynne, S.; Galea, E.R.; Owen, M.; Lawrence, P.J.; Filippidis, L. (1999). "A review of the methodologies used in the computer simulation of evacuation from the built environment".

1942: 1438: 562:). 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. 556: 2251: 2374:

and his students have pioneered agent-based models of pedestrians, an approach referred to as multi-human simulation to distinguish it from conventional crowd simulation.

2365:. 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 1469: 3788:

N. Shiwakoti et al., "Animal dynamics based approach for modeling pedestrian crowd egress under panic conditions", Transportation Research Part B 45 (2011) 1433-1449.

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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

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Kim, Sujeong; Guy, Stephen J.; Manocha, Dinesh; Lin, Ming C. (2012). "Interactive simulation of dynamic crowd behaviors using general adaptation syndrome theory".

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Durupinar, Funda; Pelechano, Nuria; Allbeck, Jan; Gudukbay, Ugur; Badler, Norman I. (2011). "How the Ocean Personality Model Affects the Perception of Crowds".

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Zhou, Suiping; Chen, Dan; Cai, Wentong; Luo, Linbo; Low, Malcolm Yoke Hean; Tian, Feng; Tay, Victor Su-Han; Ong, Darren Wee Sze; Hamilton, Benjamin D. (2010).

437:. 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 2419:

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.

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Degond, Pierre; Navoret, Laurent; Bon, Richard; Sanchez, David (2010). "Congestion in a macroscopic model of self-driven particles modeling gregariousness".

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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.

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

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Jérôme Comptdaer, Emmanuel Chiva, Stéphane Delorme, Henri Morlaye, Jérôme Volpoët, Multi-scale behavioral models for urban crisis training simulation.

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Vigueras, G.; Lozano, M.; Pérez, C.; Orduña, J.M. (2008). "A Scalable Architecture for Crowd Simulation: Implementing a Parallel Action Server".

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Most often, this approach is used for simulating realistic crowd behavior as the researcher is given complete freedom to implement any behaviors.

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Guy, Stephen J.; Kim, Sujeong; Lin, Ming C.; Manocha, Dinesh (2011). "Simulating heterogeneous crowd behaviors using personality trait theory".

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chemical in a confined space with limited exit routes has been found to have both similarities and differences to equivalent human behavior.

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Musse, S. R.; Thalmann, D. (1997). "A Model of Human Crowd Behavior : Group Inter-Relationship and Collision Detection Analysis".

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different goals. This results in more realistic crowd behavior though may be more computationally intensive than simpler techniques.

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all move along with each other. This means that 'friendship' would be the situation among the typical behavior of walking together.

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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.

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http://people.ucalgary.ca/~far/Lectures/SENG697/PDF/tutorials/2002/Multiple_Agent_Simulation_System_in_a_Virtual_Environment.pdf

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

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crowd. A brush metaphor is introduced to distribute, model and control crowd members in real-time with immediate feedback.

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Braun, A.; Musse, S.R.; De Oliveira, L.P.L.; Bodmann, B.E.J. (2003). "Modeling individual behaviors in crowd simulation".

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Particles systems have been widely used in films for effects such as explosions, for water effects in the 2000 movie

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Aubel, A.; Boulic, R.; Thalmann, D. (2000). "Real-time display of virtual humans: Levels of details and impostors".

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Chao, Qianwen; Deng, Zhigang; Jin, Xiaogang (2015). "Vehicle-pedestrian interaction for mixed traffic simulation".

2088:{\displaystyle F{\overline {a}}_{i}=K\sum \left(AL_{i}DE_{j}\times \left|d_{ij}-d_{ip}\right|\times e_{ij}\right)} 394:

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

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published following these which enhance the scalability, flexibility, applicability, and realism of simulations:

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and their movements are documented such that algorithms can be derived and implemented into crowd simulations.

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is used as the basics for crowd behavior. This same type of behavior model is used for evacuation simulations.

870:{\displaystyle I_{a}={\begin{cases}c&{\text{if }}p_{a}\in A\\0&{\text{if }}p_{a}\not \in A\end{cases}}} 4015:

Gosselin, David R.; Sander, Pedro V.; Mitchell, Jason L. (2004). "Drawing a Crowd". In Engel, Wolfgang (ed.).

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Thalmann, Daniel; Grillon, Helena; Maim, Jonathan; Yersin, Barbara (2009). "Challenges in Crowd Simulation".

306: 256: 3526:; Curtis, Sean; Lin, Ming C; Manocha, Dinesh (2011). "Directing Crowd Simulations Using Navigation Fields". 3069:"Artificial Fishes: Autonomous Locomotion, Perception, Behavior, and Learning in a Simulated Physical World" 5505: 5456: 4887: 4558: 4383: 4373: 4358: 4338: 2919:

Sewall, Jason; Wilkie, David; Lin, Ming C. (2011). "Interactive hybrid simulation of large-scale traffic".

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films, where AI armies of thousands of characters battle each other. This crowd simulation was done using

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Proceedings of the Sixth AAAI Conference On Artificial Intelligence and Interactive Digital Entertainment

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

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large and dense crowd in a given environment. Best used in studying large crowd, short time objectives.

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Sung, Mankyu; Gleicher, Michael; Chenney, Stephen (2004). "Scalable behaviors for crowd simulation".

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S. Wang et al., "Behavior of Ants Escaping from a Single-Exit Room", PLoS One. 2015; 10(6): e0131784.

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

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

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Tecchia, Franco; Loscos, Celine; Chrysanthou, Yiorgos (2002). "Visualizing Crowds in Real-Time".

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Crowd simulation may focus on aspects that target different applications. For realistic and fast

158: 3155: 2955: 5891: 5551: 5424: 5129: 4907: 4860: 4275: 4250: 4126: 3678: 3535: 3159: 3083: 3021: 2959: 2792: 2674:"Design and Evaluation of a Real-World Virtual Environment for Architecture and Urban Planning" 2576: 310: 295: 173: 1445: 5700: 5605: 5255: 5224: 5214: 5139: 5099: 4870: 4639: 4205: 2312:'bus stop' as the situation if the behavior of the agents are to be getting on or off a bus. 1746: 1127: 487:

Patils algorithm's most important and distinctive feature is that it utilizes the concept of

318: 138: 4016: 3477:"From crowd simulation to airbag deployment: particle systems, a new paradigm of simulation" 3147: 2947: 1726: 713:

is the intensity of the time pressure as a function of the estimated time to reach the goal

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Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation – SCA '11

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Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation – SCA '04

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Aschwanden, Gideon. Halatsch, Jan. Schmitt, Gerhard. Crowd Simulation for Urban Planning.

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Proceedings of the 14th annual conference on Computer graphics and interactive techniques

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structure. Each agent (individual) can be defined according to the following parameters:

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Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games – I3D '12

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

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are used, while variations (changes) in appearance help present a realistic population.

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Ulicny, Branislav; Ciechomski, Pablo de Heras; Thalmann, Daniel (2004). "Crowdbrush".

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Kallmann, Marcelo; Thalmann, Daniel (1999). "Modeling Objects for Interaction Tasks".

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Reynolds, Craig (1987). "Flocks, herds and schools: A distributed behavioral model".

3000: 2977: 2858: 2810: 2633: 2547: 4113:, An open-source framework for developing and evaluating crowd simulation algorithms 3954: 3904: 3847: 3775: 3412: 3353: 2905: 2870: 2841:

Maim, J.; Yersin, B.; Thalmann, D. (2009). "Unique Character Instances for Crowds".

2824: 2770: 2700: 1382:

is the preferred number of neighbors within a unit space for that particular agent.

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AL – Altruism level representing the tendency to help other agents. Values

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DE – Dependence level of the agent which mimics the need for help. Values

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Drettakis, George; Roussou, Maria; Reche, Alex; Tsingos, Nicolas (2007).

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Torrey, L. Crowd Simulation Via Multi-agent Reinforcement Learning. In:

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Proc. ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA'07)

3345: 3238: 3035: 5688: 5666: 5287: 5154: 5067: 4750: 4745: 4690: 3863:"Modeling Crowd and Trained Leader Behavior during Building Evacuation" 3642: 2806: 299: 214: 148: 4001: 3887: 3500: 2395:, where the lower the need lies in the hierarchy, the stronger it is. 596:

The behavior of crowds in high-stress situations can be modeled using

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Davis Guy. Multiple Agent Simulation System in a Virtual Environment.

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Proceedings 11th IEEE International Workshop on Program Comprehension

3223:"Hierarchical model for real time simulation of virtual human crowds" 2897: 2189:. When both agents are close enough to each other, the one with high 1804: 361:

Initial research in the field of crowd simulation began in 1997 with

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http://papers.cumincad.org/data/works/att/ecaade2008_160.content.pdf

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is a much stronger need, causing him to act according to that need.

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is the distance vector point from the agents to the door's position

1763:

is the maximum rate at which an agent's stress response can change.

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http://cc.ist.psu.edu/BRIMS/archives/2007/papers/07-BRIMS-025.pdf

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is critically important in creating the layout of the building.

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IEEE Transactions on Circuits and Systems for Video Technology

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Terzopoulos, Demetri; Tu, Xiaoyuan; Grzeszczuk, Radek (1994).

345:

In 1987, behavioral animation was introduced and developed by

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ShaderX3: Advanced Rendering Techniques in DirectX and OpenGL

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across many environments and building types. Individuals are

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The following function outlines the bulk of the algorithm:

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is the current number of neighbors within a unit space and

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In games and applications intended to replicate real-life

5272: 3920:

2008 37th International Conference on Parallel Processing

3917: 2786: 1211:{\displaystyle I_{p}={\mathcal {N}}(p_{a}-p_{s},\sigma )} 3740:

IEEE Transactions on Visualization and Computer Graphics

3528:

IEEE Transactions on Visualization and Computer Graphics

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IEEE Transactions on Visualization and Computer Graphics

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Examples of notable crowd AI simulation can be seen in

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The running time of computing the navigation field is

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behave as according to the city's design and events.

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To model the effect of the dependence parameter with

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ACM Transactions on Modeling and Computer Simulation

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Thalmann, Daniel; Musse, Soraia Raupp (2012-10-04).

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is the unitary vector with the origin at position i.

1723:

is the stress response capped at a maximum value of

5352:

List of animated television series by episode count

4116: 3987: 2253:). This means that the evacuation ability of agent 34:

may be too technical for most readers to understand

5927:Task allocation and partitioning of social insects 2840: 2777: 2245: 2087: 1936: 1755: 1735: 1715: 1693: 1523: 1503: 1483: 1463: 1432: 1374: 1347: 1320: 1291: 1210: 1136: 1116: 1089: 1062: 1033: 965: 945: 925: 898: 869: 759: 732: 705: 676: 550: 2836: 2834: 2350:movement speeds and densities, and anomalies are 5939: 3726: 3672: 3578: 2945: 2681:Presence: Teleoperators and Virtual Environments 2451: 1831: 1328:is the intensity of the interpersonal stressor, 565: 558:, where m × n is the grid dimension (similar to 5347:List of animated films by box office admissions 4023:. Cambridge, MA: Charles River Media. pp. 3860: 3733:"Door and doorway etiquette for virtual humans" 3428: 2918: 2500: 2370:to its changes, and react to the other agents. 2361:Individual entities in a crowd are also called 1531:is an exponent depending on the stressor type. 1034:{\displaystyle I_{p}=\lVert p_{a}-p_{s}\rVert } 433:One way to simulate virtual crowds is to use a 3316: 3198:"Steering Behaviors For Autonomous Characters" 2831: 286:is the process of simulating the movement (or 5425: 4171: 3948: 3220: 3145: 3118: 2620:Thalmann, Daniel (2016). "Crowd Simulation". 2276:Leader behavior during evacuation simulations 1070:is the intensity of the positional stressor, 264: 3448:(5-6 "Special Issue on SCA 2005"): 246–274. 3319:"Crowd modeling and simulation technologies" 2789:2009 International Conference on CyberWorlds 2563: 1028: 1002: 3475:Cohen, Eyal; Najman, Laurent (1997-01-01). 3474: 2883: 2622:Encyclopedia of Computer Graphics and Games 2201:in this example) adopts the value of agent 1471:is the perceived stress for a stress level 574: 5432: 5418: 4178: 4164: 2460: 271: 257: 86: 4130: 3886: 3861:Pelechano, Nuria; Badler, Norman (2006). 3682: 3641: 3539: 3386: 3163: 3125:. Springer Science & Business Media. 3087: 3025: 2997:http://dl.acm.org/citation.cfm?id=1272705 2963: 2796: 2465: 1538:can be found with the following formula: 62:Learn how and when to remove this message 46:, without removing the technical details. 3722: 3720: 3015: 2619: 2381: 2330: 2150:Consequently, the greater the parameter 1292:{\displaystyle I_{i}=max(n_{c}-n_{p},0)} 933:is the position of the agent in an area 677:{\displaystyle I_{t}=max(t_{e}-t_{a},0)} 3867:IEEE Computer Graphics and Applications 3622:IEEE Computer Graphics and Applications 3020:. Vol. July 1987. pp. 25–34. 2843:IEEE Computer Graphics and Applications 2299: 1795:can also refer to simulations based on 906:is the intensity of the area pressure, 5940: 3360: 5535:Patterns of self-organization in ants 5413: 4159: 3815: 3813: 3717: 3424: 3422: 3150:Computer Animation and Simulation '97 2950:Computer Animation and Simulation '98 2886:Computer Animation and Virtual Worlds 591: 465:, and simulated gas in the 1994 film 313:, reduction of the complexity of the 44:make it understandable to non-experts 5320:Films with live action and animation 4185: 18: 2473:The Lord of the Rings (film series) 1937:{\displaystyle v_{i}=(1-DE)v_{max}} 479:Algorithm by Patil and Van Den Berg 428: 13: 5545:symmetry breaking of escaping ants 3964:. San Diego, CA. pp. 119–128. 3942: 3810: 3419: 3221:Musse, S.R.; Thalmann, D. (2001). 1168: 14: 5974: 4412:Modern TV cable and streaming era 4104: 2486: 2327:Human-like behaviors and crowd AI 1097:is the position of the agent and 389: 5582: 5388: 5379: 5378: 4141:10.1111/j.1467-8659.2004.00783.x 2377: 2265:and both start moving together. 298:for visual media like films and 23: 4065: 4053: 4041: 4008: 3981: 3978:. AAAI Press, Menlo Park (2010) 3968: 3911: 3854: 3800: 3791: 3782: 3666: 3613: 3572: 3515: 3468: 3310: 3267: 3245: 3214: 3190: 3139: 3112: 3060: 3009: 2990: 2939: 1433:{\displaystyle \psi (I)=kI^{n}} 1393:and is modeled by the formula: 309:of a crowd for visual media or 3370:Journal of Statistical Physics 2912: 2877: 2742: 2707: 2665: 2646: 2630:10.1007/978-3-319-08234-9_69-1 2613: 2517: 2406: 2127:of the simulation environment; 1915: 1900: 1877:, the equation is defined as: 1639: 1601: 1458: 1452: 1411: 1405: 1286: 1254: 1205: 1173: 671: 639: 551:{\displaystyle O(m*n*log(mn))} 545: 542: 533: 509: 1: 5342:Most expensive animated films 4997:Direct manipulation animation 4648:Barrier-grid and stereography 3481:Journal of Electronic Imaging 2736:10.1016/S0360-1323(98)00057-2 2607: 2479:plugin for crowd simulation, 2452:Crowd rendering and animation 2422: 2246:{\displaystyle DE_{j}=DE_{i}} 1832:Modeling individual behaviors 566:Individual behavior modelling 5506:Mixed-species foraging flock 5457:Agent-based model in biology 5439: 4888:Non-photorealistic rendering 2921:ACM Transactions on Graphics 2501:Evacuation and riot handling 2443:Q(s, a) ←− r + maxaQ(s', a') 1971: 1787: 448:Usually each particle has a 7: 5753:Particle swarm optimization 4520:International Animation Day 3433:(September–November 2007). 3174:10.1007/978-3-7091-6874-5_3 2974:10.1007/978-3-7091-6375-7_6 2526: 2508: 2393:Maslow's hierarchy of needs 2386:Maslow's Hierarchy of Needs 598:General Adaptation Syndrome 190:Computer-generated imagery 10: 5979: 5462:Collective animal behavior 4990:Linear Animation Generator 4893:Physically based animation 3454:10.1016/j.gmod.2007.09.001 2342: 2280:As described earlier, the 2180:and has the high level of 2174:which points to the agent 336: 5854: 5816: 5771: 5723: 5591: 5580: 5447: 5374: 5243: 5172: 5045: 4970: 4920: 4826: 4778: 4759: 4681: 4638: 4631: 4579:Children's animated films 4528: 4426: 4309: 4201: 4194: 3830:10.1109/CASA.2003.1199317 3752:10.1109/TVCG.2018.2874050 3405:10.1007/s10955-009-9879-x 3154:. Eurographics. pp. 3098:10.1162/artl.1994.1.4.327 2954:. Eurographics. pp. 2564:Crowd simulation software 5791:Self-propelled particles 4488:Animation film festivals 4087:10.1177/1046878107308092 3435:"Autonomous pedestrians" 3229:(Submitted manuscript). 2716:Building and Environment 1811:Emergency response teams 1464:{\displaystyle \psi (I)} 575:Personality-based models 5872:Collective intelligence 5738:Ant colony optimization 5310:Twelve basic principles 5230:Instructional animation 4119:Computer Graphics Forum 4075:Simulation & Gaming 3693:10.1145/2159616.2159626 3589:10.1145/2019406.2019413 3338:10.1145/1842722.1842725 3286:10.1145/1028523.1028555 2933:10.1145/2070781.2024169 2763:10.1111/1467-8659.00633 2751:Computer Graphics Forum 2538:Artificial intelligence 2461:Real world applications 1756:{\displaystyle \alpha } 1511:is a scale factor, and 1221:Interpersonal stressors 1137:{\displaystyle \sigma } 585:OCEAN personality model 5892:Microbial intelligence 5552:Shoaling and schooling 4908:Virtual cinematography 4505:Highest-grossing films 4407:Early TV broadcast era 2577:Moving Picture Company 2575:Alice Software by the 2466:Virtual cinematography 2387: 2340: 2335:A crowd simulation of 2247: 2089: 1938: 1757: 1737: 1736:{\displaystyle \beta } 1717: 1695: 1525: 1505: 1485: 1465: 1434: 1376: 1349: 1322: 1293: 1212: 1138: 1118: 1091: 1064: 1035: 967: 947: 927: 900: 871: 761: 740:and a time constraint 734: 707: 678: 552: 311:virtual cinematography 174:Virtual cinematography 78:Three-dimensional (3D) 5225:Independent animation 5215:Educational animation 2693:10.1162/pres.16.3.318 2385: 2334: 2316:Non-Spatial situation 2259:is shared with agent 2248: 2165:, the bigger will be 2090: 1939: 1853:Id – Agent identifier 1773:The Lord of the Rings 1758: 1738: 1718: 1696: 1526: 1506: 1486: 1466: 1435: 1377: 1375:{\displaystyle n_{p}} 1350: 1348:{\displaystyle n_{c}} 1323: 1321:{\displaystyle I_{i}} 1294: 1213: 1139: 1119: 1117:{\displaystyle p_{s}} 1092: 1090:{\displaystyle p_{a}} 1065: 1063:{\displaystyle I_{p}} 1036: 968: 948: 928: 901: 872: 762: 760:{\displaystyle t_{a}} 735: 708: 679: 553: 411:Entity-based Approach 371:real time simulations 319:image-based rendering 139:Computer-aided design 5958:3D computer graphics 5912:Spatial organization 5877:Decentralised system 5715:Sea turtle migration 5569:Swarming (honey bee) 5205:Animated documentary 5037:Whiteboard animation 4930:Traditional puppetry 4574:Adult animated films 4483:Biologist simulators 4446:Animation department 3951:Terzopoulos, Demetri 3928:10.1109/ICPP.2008.20 3879:10.1109/MCG.2006.133 3729:Terzopoulos, Demetri 3634:10.1109/MCG.2009.105 3550:10.1109/TVCG.2010.33 3431:Terzopoulos, Demetri 2855:10.1109/MCG.2009.129 2533:3D computer graphics 2337:Covent Garden square 2300:Scalable simulations 2211: 1959: 1884: 1869:– Speed of the agent 1747: 1727: 1707: 1544: 1515: 1495: 1475: 1446: 1399: 1359: 1332: 1305: 1229: 1150: 1128: 1101: 1074: 1047: 986: 977:Positional stressors 957: 937: 910: 883: 780: 744: 717: 690: 614: 560:Dijkstra's algorithm 503: 420:Agent-based Approach 5887:Group size measures 5449:Biological swarming 5278:Character animation 5058:Character animation 4596:Children's animated 3493:1997JEI.....6...94B 3397:2010JSP...138...85D 3280:. pp. 243–52. 3239:10.1109/2945.928167 3036:10.1145/37401.37406 2728:1999BuEnv..34..741G 1839:Navigation function 402:Flow-based Approach 355:Demetri Terzopoulos 240:Global illumination 164:Virtual engineering 5953:Computer animation 5902:Predator satiation 5763:Swarm (simulation) 5758:Swarm intelligence 5733:Agent-based models 5564:Swarming behaviour 5305:Creature animation 5235:Virtual newscaster 5180:Abstract animation 5012:Ink-wash animation 5002:Humanoid animation 4985:Audio-Animatronics 4549:Lost or unfinished 4473:Animation database 4451:Animation director 3922:. pp. 430–7. 3824:. pp. 143–8. 3677:. pp. 55–62. 3583:. pp. 43–52. 2807:10.1109/CW.2009.23 2658:2016-12-21 at the 2597:Quadstone Paramics 2587:Massive (software) 2553:Multi-agent system 2388: 2367:agent-based model. 2345:Swarm intelligence 2341: 2243: 2085: 1934: 1803:, and even social 1753: 1733: 1713: 1691: 1686: 1521: 1501: 1481: 1461: 1430: 1372: 1345: 1318: 1289: 1208: 1134: 1114: 1087: 1060: 1031: 963: 943: 926:{\textstyle p_{a}} 923: 899:{\textstyle I_{a}} 896: 867: 862: 757: 733:{\textstyle t_{e}} 730: 706:{\textstyle I_{t}} 703: 674: 592:Stress-based model 548: 5935: 5934: 5922:Military swarming 5867:Animal navigation 5786:Collective motion 5773:Collective motion 5640:reverse migration 5574:Swarming motility 5407: 5406: 5168: 5167: 5095:Erasure animation 4916: 4915: 4658:Limited animation 4601:Computer-animated 4539:Computer-animated 4461:Animation studios 4422: 4421: 4002:10.1109/76.825720 3839:978-0-7695-1934-0 3702:978-1-4503-1194-6 3598:978-1-4503-0923-3 3524:Van Den Berg, Jur 3501:10.1117/12.261175 3295:978-3-905673-14-2 3183:978-3-211-83048-2 3132:978-1-4471-4449-6 3005:978-1-59593-624-0 2983:978-3-211-83257-8 2816:978-1-4244-4864-7 2791:. pp. 1–12. 2639:978-3-319-08234-9 2548:Emergent behavior 2309:Spatial situation 1974: 1875:individual agents 1716:{\displaystyle S} 1673: 1647: 1631: 1586: 1565: 1524:{\displaystyle n} 1504:{\displaystyle k} 1484:{\displaystyle I} 966:{\displaystyle c} 946:{\displaystyle A} 842: 812: 489:navigation fields 462:The Perfect Storm 281: 280: 80:computer graphics 72: 71: 64: 16:Model of movement 5970: 5748:Crowd simulation 5725:Swarm algorithms 5696:Insect migration 5601:Animal migration 5593:Animal migration 5586: 5511:Mobbing behavior 5434: 5427: 5420: 5411: 5410: 5392: 5382: 5381: 5362:anime franchises 5337:Cartoon violence 5325:highest grossing 5220:Erotic animation 5195:Animated cartoon 4962:Supermarionation 4935:Digital puppetry 4856:Facial animation 4776: 4775: 4636: 4635: 4509:Opening weekends 4199: 4198: 4180: 4173: 4166: 4157: 4156: 4152: 4134: 4099: 4098: 4069: 4063: 4057: 4051: 4045: 4039: 4038: 4022: 4012: 4006: 4005: 3985: 3979: 3972: 3966: 3965: 3959: 3946: 3940: 3939: 3915: 3909: 3908: 3890: 3858: 3852: 3851: 3817: 3808: 3804: 3798: 3795: 3789: 3786: 3780: 3779: 3746:(3): 1502–1517. 3737: 3724: 3715: 3714: 3686: 3670: 3664: 3663: 3645: 3617: 3611: 3610: 3576: 3570: 3569: 3543: 3519: 3513: 3512: 3472: 3466: 3465: 3442:Graphical Models 3439: 3426: 3417: 3416: 3390: 3364: 3358: 3357: 3323: 3314: 3308: 3307: 3271: 3265: 3264: 3249: 3243: 3242: 3218: 3212: 3211: 3209: 3208: 3194: 3188: 3187: 3167: 3153: 3143: 3137: 3136: 3122:Crowd Simulation 3116: 3110: 3109: 3091: 3073: 3064: 3058: 3057: 3029: 3013: 3007: 2994: 2988: 2987: 2967: 2953: 2943: 2937: 2936: 2916: 2910: 2909: 2898:10.1002/cav.1654 2881: 2875: 2874: 2838: 2829: 2828: 2800: 2784: 2775: 2774: 2746: 2740: 2739: 2711: 2705: 2704: 2678: 2669: 2663: 2650: 2644: 2643: 2624:. pp. 1–8. 2617: 2264: 2258: 2252: 2250: 2249: 2244: 2242: 2241: 2226: 2225: 2206: 2200: 2194: 2188: 2179: 2173: 2164: 2158: 2145: 2134: 2126: 2120: 2109: 2094: 2092: 2091: 2086: 2084: 2080: 2079: 2078: 2063: 2059: 2058: 2057: 2042: 2041: 2021: 2020: 2008: 2007: 1981: 1980: 1975: 1967: 1943: 1941: 1940: 1935: 1933: 1932: 1896: 1895: 1801:crowd psychology 1793:Crowd simulation 1782:Massive software 1762: 1760: 1759: 1754: 1742: 1740: 1739: 1734: 1722: 1720: 1719: 1714: 1700: 1698: 1697: 1692: 1690: 1689: 1674: 1671: 1648: 1645: 1632: 1630: 1622: 1614: 1587: 1584: 1566: 1564: 1556: 1548: 1530: 1528: 1527: 1522: 1510: 1508: 1507: 1502: 1490: 1488: 1487: 1482: 1470: 1468: 1467: 1462: 1439: 1437: 1436: 1431: 1429: 1428: 1387:perceived stress 1381: 1379: 1378: 1373: 1371: 1370: 1354: 1352: 1351: 1346: 1344: 1343: 1327: 1325: 1324: 1319: 1317: 1316: 1298: 1296: 1295: 1290: 1279: 1278: 1266: 1265: 1241: 1240: 1217: 1215: 1214: 1209: 1198: 1197: 1185: 1184: 1172: 1171: 1162: 1161: 1143: 1141: 1140: 1135: 1123: 1121: 1120: 1115: 1113: 1112: 1096: 1094: 1093: 1088: 1086: 1085: 1069: 1067: 1066: 1061: 1059: 1058: 1040: 1038: 1037: 1032: 1027: 1026: 1014: 1013: 998: 997: 972: 970: 969: 964: 952: 950: 949: 944: 932: 930: 929: 924: 922: 921: 905: 903: 902: 897: 895: 894: 876: 874: 873: 868: 866: 865: 853: 852: 843: 840: 823: 822: 813: 810: 792: 791: 766: 764: 763: 758: 756: 755: 739: 737: 736: 731: 729: 728: 712: 710: 709: 704: 702: 701: 683: 681: 680: 675: 664: 663: 651: 650: 626: 625: 557: 555: 554: 549: 429:Particle systems 292: 291: 284:Crowd simulation 273: 266: 259: 245:Volume rendering 235:Crowd simulation 220:Wire-frame model 193: 90: 74: 73: 67: 60: 56: 53: 47: 27: 26: 19: 5978: 5977: 5973: 5972: 5971: 5969: 5968: 5967: 5938: 5937: 5936: 5931: 5850: 5812: 5767: 5719: 5587: 5578: 5443: 5438: 5408: 5403: 5370: 5332:Cartoon physics 5251:Animation music 5239: 5200:Animated sitcom 5190:Adult animation 5164: 5145:Special effects 5041: 4966: 4912: 4822: 4763: 4755: 4677: 4627: 4606:Direct-to-video 4524: 4418: 4305: 4190: 4184: 4107: 4102: 4070: 4066: 4058: 4054: 4046: 4042: 4035: 4013: 4009: 3986: 3982: 3973: 3969: 3957: 3953:(August 2007). 3947: 3943: 3916: 3912: 3859: 3855: 3840: 3818: 3811: 3805: 3801: 3796: 3792: 3787: 3783: 3735: 3727:Huang, Wenjia; 3725: 3718: 3703: 3684:10.1.1.673.3693 3671: 3667: 3618: 3614: 3599: 3577: 3573: 3541:10.1.1.183.7823 3522:Patil, Sachin; 3520: 3516: 3473: 3469: 3437: 3427: 3420: 3365: 3361: 3321: 3315: 3311: 3296: 3272: 3268: 3261: 3250: 3246: 3219: 3215: 3206: 3204: 3196: 3195: 3191: 3184: 3144: 3140: 3133: 3117: 3113: 3076:Artificial Life 3071: 3065: 3061: 3046: 3027:10.1.1.103.7187 3014: 3010: 2995: 2991: 2984: 2965:10.1.1.550.2013 2944: 2940: 2917: 2913: 2892:(3–4): 405–12. 2882: 2878: 2839: 2832: 2817: 2798:10.1.1.365.5045 2785: 2778: 2747: 2743: 2712: 2708: 2676: 2670: 2666: 2660:Wayback Machine 2651: 2647: 2640: 2618: 2614: 2610: 2566: 2558:Particle system 2529: 2520: 2511: 2503: 2489: 2468: 2463: 2454: 2425: 2409: 2380: 2347: 2329: 2302: 2278: 2260: 2254: 2237: 2233: 2221: 2217: 2212: 2209: 2208: 2202: 2196: 2190: 2186: 2181: 2175: 2171: 2166: 2160: 2156: 2151: 2143: 2138: 2130: 2122: 2118: 2113: 2107: 2102: 2071: 2067: 2050: 2046: 2034: 2030: 2029: 2025: 2016: 2012: 2003: 1999: 1995: 1991: 1976: 1966: 1965: 1960: 1957: 1956: 1922: 1918: 1891: 1887: 1885: 1882: 1881: 1868: 1834: 1790: 1768:New Line Cinema 1748: 1745: 1744: 1728: 1725: 1724: 1708: 1705: 1704: 1685: 1684: 1670: 1668: 1659: 1658: 1644: 1642: 1623: 1615: 1613: 1598: 1597: 1583: 1581: 1571: 1570: 1557: 1549: 1547: 1545: 1542: 1541: 1536:stress response 1516: 1513: 1512: 1496: 1493: 1492: 1476: 1473: 1472: 1447: 1444: 1443: 1424: 1420: 1400: 1397: 1396: 1366: 1362: 1360: 1357: 1356: 1339: 1335: 1333: 1330: 1329: 1312: 1308: 1306: 1303: 1302: 1274: 1270: 1261: 1257: 1236: 1232: 1230: 1227: 1226: 1193: 1189: 1180: 1176: 1167: 1166: 1157: 1153: 1151: 1148: 1147: 1129: 1126: 1125: 1108: 1104: 1102: 1099: 1098: 1081: 1077: 1075: 1072: 1071: 1054: 1050: 1048: 1045: 1044: 1022: 1018: 1009: 1005: 993: 989: 987: 984: 983: 973:is a constant. 958: 955: 954: 938: 935: 934: 917: 913: 911: 908: 907: 890: 886: 884: 881: 880: 861: 860: 848: 844: 839: 837: 831: 830: 818: 814: 809: 807: 797: 796: 787: 783: 781: 778: 777: 751: 747: 745: 742: 741: 724: 720: 718: 715: 714: 697: 693: 691: 688: 687: 659: 655: 646: 642: 621: 617: 615: 612: 611: 594: 577: 568: 504: 501: 500: 481: 454:position vector 450:velocity vector 435:particle system 431: 392: 363:Daniel Thalmann 339: 331:social dynamics 289: 288: 277: 225:Texture mapping 191: 169:Virtual reality 79: 68: 57: 51: 48: 40:help improve it 37: 28: 24: 17: 12: 11: 5: 5976: 5966: 5965: 5963:Social physics 5960: 5955: 5950: 5933: 5932: 5930: 5929: 5924: 5919: 5914: 5909: 5907:Quorum sensing 5904: 5899: 5894: 5889: 5884: 5879: 5874: 5869: 5864: 5858: 5856: 5855:Related topics 5852: 5851: 5849: 5848: 5843: 5841:Swarm robotics 5838: 5833: 5828: 5822: 5820: 5818:Swarm robotics 5814: 5813: 5811: 5810: 5805: 5800: 5799: 5798: 5788: 5783: 5777: 5775: 5769: 5768: 5766: 5765: 5760: 5755: 5750: 5745: 5740: 5735: 5729: 5727: 5721: 5720: 5718: 5717: 5712: 5711: 5710: 5709: 5708: 5693: 5692: 5691: 5686: 5676: 5675: 5674: 5669: 5664: 5659: 5652:Fish migration 5649: 5647:Cell migration 5644: 5643: 5642: 5637: 5630:Bird migration 5627: 5626: 5625: 5623:coded wire tag 5620: 5619: 5618: 5608: 5597: 5595: 5589: 5588: 5581: 5579: 5577: 5576: 5571: 5566: 5561: 5560: 5559: 5549: 5548: 5547: 5542: 5532: 5531: 5530: 5520: 5519: 5518: 5516:feeding frenzy 5508: 5503: 5498: 5497: 5496: 5486: 5485: 5484: 5479: 5469: 5464: 5459: 5453: 5451: 5445: 5444: 5437: 5436: 5429: 5422: 5414: 5405: 5404: 5402: 5401: 5396: 5386: 5375: 5372: 5371: 5369: 5368: 5367: 5366: 5365: 5364: 5349: 5344: 5339: 5334: 5329: 5328: 5327: 5317: 5312: 5307: 5302: 5301: 5300: 5295: 5290: 5285: 5275: 5270: 5265: 5264: 5263: 5261:Mickey Mousing 5258: 5247: 5245: 5244:Related topics 5241: 5240: 5238: 5237: 5232: 5227: 5222: 5217: 5212: 5207: 5202: 5197: 5192: 5187: 5176: 5174: 5170: 5169: 5166: 5165: 5163: 5162: 5157: 5152: 5147: 5142: 5137: 5135:Straight ahead 5132: 5127: 5122: 5117: 5115:Paint-on-glass 5112: 5107: 5102: 5097: 5092: 5087: 5082: 5081: 5080: 5075: 5070: 5065: 5055: 5049: 5047: 5043: 5042: 5040: 5039: 5034: 5032:Squigglevision 5029: 5024: 5019: 5014: 5009: 5007:Idle animation 5004: 4999: 4994: 4993: 4992: 4987: 4976: 4974: 4968: 4967: 4965: 4964: 4959: 4958: 4957: 4952: 4947: 4942: 4932: 4926: 4924: 4918: 4917: 4914: 4913: 4911: 4910: 4905: 4900: 4895: 4890: 4885: 4884: 4883: 4878: 4873: 4866:Motion capture 4863: 4858: 4853: 4848: 4843: 4838: 4832: 4830: 4824: 4823: 4821: 4820: 4818:Onion skinning 4815: 4810: 4805: 4800: 4795: 4790: 4784: 4782: 4773: 4757: 4756: 4754: 4753: 4748: 4743: 4742: 4741: 4731: 4730: 4729: 4719: 4714: 4704: 4703: 4702: 4687: 4685: 4679: 4678: 4676: 4675: 4673:Exposure sheet 4670: 4665: 4660: 4655: 4650: 4644: 4642: 4633: 4629: 4628: 4626: 4625: 4624: 4623: 4618: 4613: 4608: 4603: 4598: 4593: 4591:Adult animated 4583: 4582: 4581: 4576: 4571: 4566: 4561: 4556: 4551: 4546: 4544:Feature-length 4541: 4532: 4530: 4526: 4525: 4523: 4522: 4517: 4512: 4502: 4501: 4500: 4495: 4485: 4480: 4475: 4470: 4469: 4468: 4458: 4453: 4448: 4443: 4442: 4441: 4430: 4428: 4424: 4423: 4420: 4419: 4417: 4416: 4415: 4414: 4409: 4404: 4399: 4397:The Golden Age 4394: 4388:United States 4386: 4384:United Kingdom 4381: 4376: 4371: 4366: 4361: 4356: 4351: 4346: 4341: 4336: 4331: 4326: 4321: 4315: 4313: 4307: 4306: 4304: 4303: 4298: 4293: 4288: 4283: 4278: 4273: 4268: 4263: 4258: 4253: 4248: 4243: 4238: 4233: 4228: 4223: 4218: 4213: 4208: 4202: 4196: 4192: 4191: 4183: 4182: 4175: 4168: 4160: 4154: 4153: 4132:10.1.1.10.2516 4114: 4106: 4105:External links 4103: 4101: 4100: 4064: 4052: 4040: 4033: 4007: 3980: 3967: 3941: 3910: 3853: 3838: 3809: 3799: 3790: 3781: 3731:(March 2020). 3716: 3701: 3665: 3612: 3597: 3571: 3514: 3467: 3418: 3359: 3309: 3294: 3266: 3259: 3244: 3213: 3189: 3182: 3165:10.1.1.14.7049 3138: 3131: 3111: 3089:10.1.1.33.8131 3059: 3045:978-0897912273 3044: 3008: 2989: 2982: 2938: 2911: 2876: 2830: 2815: 2776: 2741: 2706: 2664: 2645: 2638: 2611: 2609: 2606: 2605: 2604: 2599: 2594: 2589: 2584: 2579: 2573: 2571:Mott MacDonald 2565: 2562: 2561: 2560: 2555: 2550: 2545: 2543:Crowd analysis 2540: 2535: 2528: 2525: 2519: 2516: 2510: 2507: 2502: 2499: 2488: 2487:Urban planning 2485: 2467: 2464: 2462: 2459: 2453: 2450: 2446: 2445: 2424: 2421: 2408: 2405: 2379: 2376: 2343:Main article: 2328: 2325: 2301: 2298: 2277: 2274: 2240: 2236: 2232: 2229: 2224: 2220: 2216: 2184: 2169: 2154: 2148: 2147: 2141: 2136: 2135:is a constant; 2128: 2116: 2111: 2105: 2096: 2095: 2083: 2077: 2074: 2070: 2066: 2062: 2056: 2053: 2049: 2045: 2040: 2037: 2033: 2028: 2024: 2019: 2015: 2011: 2006: 2002: 1998: 1994: 1990: 1987: 1984: 1979: 1973: 1970: 1964: 1945: 1944: 1931: 1928: 1925: 1921: 1917: 1914: 1911: 1908: 1905: 1902: 1899: 1894: 1890: 1871: 1870: 1866: 1863: 1860: 1857: 1854: 1833: 1830: 1797:group dynamics 1789: 1786: 1752: 1732: 1712: 1688: 1683: 1680: 1677: 1669: 1667: 1664: 1661: 1660: 1657: 1654: 1651: 1643: 1641: 1638: 1635: 1629: 1626: 1621: 1618: 1612: 1609: 1606: 1603: 1600: 1599: 1596: 1593: 1590: 1582: 1580: 1577: 1576: 1574: 1569: 1563: 1560: 1555: 1552: 1520: 1500: 1480: 1460: 1457: 1454: 1451: 1427: 1423: 1419: 1416: 1413: 1410: 1407: 1404: 1369: 1365: 1342: 1338: 1315: 1311: 1288: 1285: 1282: 1277: 1273: 1269: 1264: 1260: 1256: 1253: 1250: 1247: 1244: 1239: 1235: 1207: 1204: 1201: 1196: 1192: 1188: 1183: 1179: 1175: 1170: 1165: 1160: 1156: 1133: 1111: 1107: 1084: 1080: 1057: 1053: 1030: 1025: 1021: 1017: 1012: 1008: 1004: 1001: 996: 992: 962: 942: 920: 916: 893: 889: 864: 859: 856: 851: 847: 838: 836: 833: 832: 829: 826: 821: 817: 808: 806: 803: 802: 800: 795: 790: 786: 754: 750: 727: 723: 700: 696: 673: 670: 667: 662: 658: 654: 649: 645: 641: 638: 635: 632: 629: 624: 620: 593: 590: 576: 573: 567: 564: 547: 544: 541: 538: 535: 532: 529: 526: 523: 520: 517: 514: 511: 508: 480: 477: 430: 427: 426: 425: 421: 417: 416: 412: 408: 407: 403: 391: 390:Crowd dynamics 388: 376:virtual humans 347:Craig Reynolds 338: 335: 296:virtual scenes 279: 278: 276: 275: 268: 261: 253: 250: 249: 248: 247: 242: 237: 232: 230:Motion capture 227: 222: 217: 212: 211: 210: 205: 195: 184: 183: 182:Related topics 179: 178: 177: 176: 171: 166: 161: 156: 154:Visual effects 151: 146: 144:Graphic design 141: 136: 128: 127: 123: 122: 121: 120: 115: 110: 105: 97: 96: 92: 91: 83: 82: 70: 69: 31: 29: 22: 15: 9: 6: 4: 3: 2: 5975: 5964: 5961: 5959: 5956: 5954: 5951: 5949: 5946: 5945: 5943: 5928: 5925: 5923: 5920: 5918: 5915: 5913: 5910: 5908: 5905: 5903: 5900: 5898: 5895: 5893: 5890: 5888: 5885: 5883: 5880: 5878: 5875: 5873: 5870: 5868: 5865: 5863: 5860: 5859: 5857: 5853: 5847: 5844: 5842: 5839: 5837: 5834: 5832: 5829: 5827: 5824: 5823: 5821: 5819: 5815: 5809: 5806: 5804: 5801: 5797: 5794: 5793: 5792: 5789: 5787: 5784: 5782: 5781:Active matter 5779: 5778: 5776: 5774: 5770: 5764: 5761: 5759: 5756: 5754: 5751: 5749: 5746: 5744: 5741: 5739: 5736: 5734: 5731: 5730: 5728: 5726: 5722: 5716: 5713: 5707: 5704: 5703: 5702: 5699: 5698: 5697: 5694: 5690: 5687: 5685: 5682: 5681: 5680: 5677: 5673: 5670: 5668: 5665: 5663: 5660: 5658: 5657:diel vertical 5655: 5654: 5653: 5650: 5648: 5645: 5641: 5638: 5636: 5633: 5632: 5631: 5628: 5624: 5621: 5617: 5614: 5613: 5612: 5609: 5607: 5604: 5603: 5602: 5599: 5598: 5596: 5594: 5590: 5585: 5575: 5572: 5570: 5567: 5565: 5562: 5558: 5555: 5554: 5553: 5550: 5546: 5543: 5541: 5538: 5537: 5536: 5533: 5529: 5526: 5525: 5524: 5521: 5517: 5514: 5513: 5512: 5509: 5507: 5504: 5502: 5499: 5495: 5494:herd behavior 5492: 5491: 5490: 5487: 5483: 5480: 5478: 5475: 5474: 5473: 5470: 5468: 5465: 5463: 5460: 5458: 5455: 5454: 5452: 5450: 5446: 5442: 5435: 5430: 5428: 5423: 5421: 5416: 5415: 5412: 5400: 5397: 5395: 5391: 5387: 5385: 5377: 5376: 5373: 5363: 5360: 5359: 5358: 5355: 5354: 5353: 5350: 5348: 5345: 5343: 5340: 5338: 5335: 5333: 5330: 5326: 5323: 5322: 5321: 5318: 5316: 5313: 5311: 5308: 5306: 5303: 5299: 5296: 5294: 5291: 5289: 5286: 5284: 5281: 5280: 5279: 5276: 5274: 5271: 5269: 5266: 5262: 5259: 5257: 5256:Bouncing ball 5254: 5253: 5252: 5249: 5248: 5246: 5242: 5236: 5233: 5231: 5228: 5226: 5223: 5221: 5218: 5216: 5213: 5211: 5208: 5206: 5203: 5201: 5198: 5196: 5193: 5191: 5188: 5185: 5181: 5178: 5177: 5175: 5171: 5161: 5158: 5156: 5153: 5151: 5148: 5146: 5143: 5141: 5138: 5136: 5133: 5131: 5128: 5126: 5123: 5121: 5118: 5116: 5113: 5111: 5108: 5106: 5103: 5101: 5100:Hydrotechnics 5098: 5096: 5093: 5091: 5090:Drawn-on-film 5088: 5086: 5083: 5079: 5076: 5074: 5071: 5069: 5066: 5064: 5061: 5060: 5059: 5056: 5054: 5051: 5050: 5048: 5046:Other methods 5044: 5038: 5035: 5033: 5030: 5028: 5025: 5023: 5020: 5018: 5017:Magic Lantern 5015: 5013: 5010: 5008: 5005: 5003: 5000: 4998: 4995: 4991: 4988: 4986: 4983: 4982: 4981: 4978: 4977: 4975: 4973: 4969: 4963: 4960: 4956: 4953: 4951: 4950:Virtual human 4948: 4946: 4943: 4941: 4938: 4937: 4936: 4933: 4931: 4928: 4927: 4925: 4923: 4919: 4909: 4906: 4904: 4901: 4899: 4896: 4894: 4891: 4889: 4886: 4882: 4879: 4877: 4876:hand tracking 4874: 4872: 4869: 4868: 4867: 4864: 4862: 4859: 4857: 4854: 4852: 4849: 4847: 4844: 4842: 4839: 4837: 4834: 4833: 4831: 4829: 4825: 4819: 4816: 4814: 4811: 4809: 4806: 4804: 4801: 4799: 4796: 4794: 4791: 4789: 4786: 4785: 4783: 4781: 4777: 4774: 4771: 4767: 4762: 4758: 4752: 4749: 4747: 4744: 4740: 4737: 4736: 4735: 4732: 4728: 4725: 4724: 4723: 4720: 4718: 4715: 4712: 4708: 4705: 4701: 4697: 4696:clay painting 4694: 4693: 4692: 4689: 4688: 4686: 4684: 4680: 4674: 4671: 4669: 4666: 4664: 4661: 4659: 4656: 4654: 4651: 4649: 4646: 4645: 4643: 4641: 4637: 4634: 4630: 4622: 4619: 4617: 4614: 4612: 4609: 4607: 4604: 4602: 4599: 4597: 4594: 4592: 4589: 4588: 4587: 4584: 4580: 4577: 4575: 4572: 4570: 4567: 4565: 4562: 4560: 4557: 4555: 4552: 4550: 4547: 4545: 4542: 4540: 4537: 4536: 4534: 4533: 4531: 4527: 4521: 4518: 4516: 4513: 4510: 4506: 4503: 4499: 4496: 4494: 4493:international 4491: 4490: 4489: 4486: 4484: 4481: 4479: 4476: 4474: 4471: 4467: 4464: 4463: 4462: 4459: 4457: 4454: 4452: 4449: 4447: 4444: 4440: 4437: 4436: 4435: 4432: 4431: 4429: 4425: 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