scalable behaviors for crowd simulation

SCALABLE BEHAVIORS FOR CROWD SIMULATION

By Mankyu Sung, Michael Gleicher and Stephen Chenney

AUTHORS Mankyu Sung

Scalable, Controllable, Efficient and convincing crowd simulation (2005)

Michael Gleicher “I have a bad case of Academic Attention Deficit Disorder”

Stephen ChenneyFlow Tiles

OUTLINE Overview Related Work Low level (probabilistic action selection) High level (situations and compositions) Results Conclusion Related Future Work Assessment

OVERVIEW

Main observations: Anonymity in the

crowd what instead of who action individual

matter only in short time contribution

A character is only in a few situations at once

RELATED WORK Rules based (Reynolds)

Not scalable from authoringperspective

Hierarchical (Musse)No complex individual behaviour

Physics inspired (Helbing)Limited behaviour and interaction

Annotated environment (The Sims, Kallmann)

LOW LEVEL (PROBABILISTIC ACTION SELECTION)

To select new state evaluate all possible states withbehaviour function

Default behaviour functions: ImageLookup TargetFind Overlap

State:s = {t, p, θ, a, s-)

Pk(s) = 1 / (1 + e-αx)

LOW LEVEL (PROBABILISTIC ACTION SELECTION)

Create complex behaviour

by composition of simple

behaviours

HIGH LEVEL (SITUATIONS AND COMPOSITIONS)

Situations spatial (ATM,

crossing) non-spatial

(friendship)

When in situation: extend state graph attach sensors add event rules add behaviour

functions

Composition means union

RESULTSTested on 3 scenarios: Street environment

crossing street, traffic sign, in-a-hurry Theatre environment

horizontal queue, follow, gathering, stay-in ...

Field environment follow, group, close

RESULTS1,3 GHz processor 1GB

memory 500 agents with

increasing number of situations

increasing number of agents with 10 situations

CONCLUSION Framework can create complex

behaviours while minimising data stored in each agent

Future work: take into account multi-agent statistics

such as crowd density more efficient simulation so not all crowd

members go through simulation step at same time

explore other mechanisms to combine behaviours to avoid time scale problem

RELATED FUTURE WORK Situation Agents: Agent-based

Externalized Steering LogicSchuerman, M., Singh, S., Kapadia, M., Faloutsos P., The Journal of Computer Animation and Virtual Worlds, Special Issue CASA 2010, Wiley, pp. 1-10, 2010, in press.

Motion patches: building blocks for virtual environments annotated with motion dataLee, K. H., Choi, M. G., and Lee, J. 2006., SIGGRAPH

’06: ACM SIGGRAPH 2006 Papers, 898–906.

ASSESSMENT Goals clearly specified Situation approach seems to indeed

limit the complexity of the agents Problems and possible solutions

presented Clearly structured and well written

ASSESSMENTClaims and assumptions Anonymity justifies probabilistic

method?Not for low density crowds People stopping in middle of crosswalk Waiting for traffic light, then not moving

when it is green

ASSESSMENTImplementation details Naive default behaviours

Path planning PRM + DijkstraPRM pre-computed, no dynamic obstacle

handlingHow are states judged to make the character

move towards position? Possible local minima? Collision detection

No prediction, possible oscillations

ASSESSMENTImplementation details: extending the state graph

extension only with default graph no interaction between situations

controlling combination of behaviour functionsuse of alpha not intuitive, when to use alpha

and when to delete a behaviour

ASSESSMENTLimited experiments maximum of 10 situations maximum of 500 agents random situations added, does this

include composite situations?

ASSESSMENTImpact and applications Limitation on kind of applications

no evacuation simulation Situational approach might be a good

idea but should be combined with other methods

Inspiration for further research