Editing Crowd simulation (section)

=== Individual behavior modelling ===
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.

==== Personality-based models ====
One method of creating individualistic behavior for crowd agents is through the use of personality traits.<ref>{{cite book |doi=10.1145/2019406.2019413 |chapter=Simulating heterogeneous crowd behaviors using personality trait theory |title=Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation – SCA '11 |pages=43–52 |year=2011 |last1=Guy |first1=Stephen J. |last2=Kim |first2=Sujeong |last3=Lin |first3=Ming C. |last4=Manocha |first4=Dinesh |isbn=978-1-4503-0923-3 }}</ref> 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.

The [[Big Five personality traits|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.<ref>{{cite journal|last1=Durupinar|first1=Funda|last2=Pelechano|first2=Nuria|last3=Allbeck|first3=Jan|last4=Gudukbay|first4=Ugur|last5=Badler|first5=Norman I.|title=How the Ocean Personality Model Affects the Perception of Crowds|journal=IEEE Computer Graphics and Applications|date=2011|volume=31|issue=3|pages=22–31|doi=10.1109/MCG.2009.105|pmid=24808089|hdl=11693/11800 |hdl-access=free}}</ref>

==== Stress-based model ====
The behavior of crowds in high-stress situations can be modeled using [[General adaptation syndrome|General Adaptation Syndrome]] theory.<ref>{{cite book |doi=10.1145/2159616.2159626 |chapter=Interactive simulation of dynamic crowd behaviors using general adaptation syndrome theory |title=Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games – I3D '12 |pages=55–62 |year=2012 |last1=Kim |first1=Sujeong |last2=Guy |first2=Stephen J. |last3=Manocha |first3=Dinesh |last4=Lin |first4=Ming C. |isbn=978-1-4503-1194-6 }}</ref> 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.

''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.

''Area pressure'' 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.

''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. 


Examples of notable crowd AI simulation can be seen in [[New Line Cinema]]'s ''[[The Lord of the Rings (film series)|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)|Massive software]].