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AUTHORS
Mankyu SungScalable, 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
RESULTS
Tested 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
RESULTS
1,3 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 Logic
Schuerman, 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
ASSESSMENT
Claims 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
ASSESSMENT
Implementation 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
ASSESSMENT
Implementation 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
ASSESSMENT
Limited experiments maximum of 10 situations maximum of 500 agents random situations added, does this
include composite situations?