2.  Mankyu Sung Scalable, Controllable, Efficient and convincing crowd simulation (2005)  Michael Gleicher “I have a bad case of Academic Attention Deficit Disorder”  Stephen Chenney Flow Tiles

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

4. 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

5.  Rules based (Reynolds) Not scalable from authoring perspective  Hierarchical (Musse) No complex individual behaviour  Physics inspired (Helbing) Limited behaviour and interaction  Annotated environment (The Sims, Kallmann)

6.  To select new state evaluate all possible states with behaviour function  Default behaviour functions:  ImageLookup  TargetFind  Overlap State: s = {t, p, θ, a, s - ) P k (s) = 1 / (1 + e -αx )

7. Create complex behaviour by composition of simple behaviours

8. Situations  spatial (ATM, crossing)  non-spatial (friendship) When in situation:  extend state graph  attach sensors  add event rules  add behaviour functions Composition means union

9. 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

10. 1,3 GHz processor 1GB memory  500 agents with increasing number of situations  increasing number of agents with 10 situations

11.  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

12.  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 data Lee, K. H., Choi, M. G., and Lee, J. 2006., SIGGRAPH ’06: ACM SIGGRAPH 2006 Papers, 898–906.

13.  Goals clearly specified  Situation approach seems to indeed limit the complexity of the agents  Problems and possible solutions presented  Clearly structured and well written

14. 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

15. Implementation details  Naive default behaviours  Path planning PRM + Dijkstra PRM pre-computed, no dynamic obstacle handling How are states judged to make the character move towards position? Possible local minima?  Collision detection No prediction, possible oscillations

16. Implementation details:  extending the state graph extension only with default graph no interaction between situations  controlling combination of behaviour functions use of alpha not intuitive, when to use alpha and when to delete a behaviour

17. Limited experiments  maximum of 10 situations  maximum of 500 agents  random situations added, does this include composite situations?

18. Impact 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