1 / 31

Scalable Behaviors for Crowd Simulation

Scalable Behaviors for Crowd Simulation. Mankyu Sung Michael Gleicher Stephen Chenney University of Wisconsin- Madison www.cs.wisc.edu/graphics. The Goal: Scalable Crowd Simulation. Large Crowds Scalable performance Large Complex Environments Scalable Authoring Rich, Complex Behaviors

lashawnr
Download Presentation

Scalable Behaviors for Crowd Simulation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Scalable Behaviors forCrowd Simulation Mankyu Sung Michael Gleicher Stephen Chenney University of Wisconsin- Madison www.cs.wisc.edu/graphics

  2. The Goal:Scalable Crowd Simulation • Large Crowds • Scalable performance • Large Complex Environments • Scalable Authoring • Rich, Complex Behaviors • Scalable Behaviors We are not considering rendering!

  3. The Issues:Conflicting Goals • Large, Complex World • Rich Behaviors • But… • Fast performance (simple agents) • Reasonable authoring Must decouple AGENT complexity from WORLD complexity!

  4. An Environment Example: Model of Street Simulation: Real time, Reactive Rendering: Unreal Game Engine for playback

  5. In a hurry Scalability:Complex Environments Store Window Doorway In front of Store Window In front of Doorway Sidewalk Friends Together Bench Use crosswalk In Crosswalk Street

  6. In a hurry: Check for traffic Run across street Observation:Behavior Depends on Situation Store Window Doorway In front of Store Window Possibly: stop to window shop In front of Doorway Possibly: open door, enter Unlikely: stand blocking door Sidewalk: Walk here Friends Together: Possibly: Stop to talk Probably: Have same goal Use crosswalk: Wait for green light Start crossing In Crosswalk: Walk across street once you’ve started Street: Generally, Don’t walk here

  7. Managing Environmental Complexity:Situation-Based Approach • Many different situations • Each has a different set of local behaviors • An agent only needs a few at a time • Blend situations/behaviors together Store Window Doorway In front of Store Window Possibly: stop to window shop In front of Doorway Possibly: open door, enter Unlikely: stand blocking door Sidewalk: Walk here Friends Together: Possibly: Stop to talk Probably: Have same goal Use crosswalk: Wait for green light Start crossing

  8. Observation:Crowds are Crowds • Individuals are anonymous • Doesn’t matter what any one does • At any given time, do something reasonable • Aggregate behavior  Stochastic Control  Short term view of agent An Individual

  9. Key Ideas • Situation-Based Approach • Breaks behavior into small pieces • Extensible agents kept simple • Situation Composition • Probabalistic scheme to compose behaviors • Painting Interface • Place behaviors in world, not agents • Use Motion-Graph-based runtime • Based on Gleicher et al 2003

  10. Related Work:Inspirations from Research • Crowd Modeling(e.g. Reynolds 1987, Musse 2001, Blue 1998, Henderson, 1974, Bouvier et al. 1997, Helbing et al 2000, …) • Simple agents, simple rules • Fast, scalable, emergent patternsbut…difficult to generalize to complex behaviors • Smart Environments(e.g. Kallman et al 1998, Farenc et al. 1999, Thomas et all 2000, Michael et al 2003, …) • Objects tell agents what to do • Scalable authoring, complex actionsbut… difficult to compose or get variability

  11. Related WorkInspirations from Systems • The SIMS (Maxis/EA Games) • Smart Environments/Objects • Massive (Regeleous) • Author Scenes, not Environments • Similar Means, Different Ends • Agent-centric, composable behaviors • Other competitors seem similar Softimage Behavior, Character Studio, AI-Implant, …

  12. Situation-Based Approach:Agent Architecture • Agents: • Discrete set of actions (from mograph) • Randomly choose from distribution • Behavior functions provide distributions • All aspects of agents can be updated dynamically

  13. Situation-Based Approach:Simple Default Agents • Default agents very simple • Wander, don’t bump into things, … • Extend agents as necessary to achieve complex behaviors

  14. Situation-Based Approach:Extensible Agent • Situations extend agents • Add Actions • Add Behavior Functions • AddSensors and Rulesthat inform Behavior Functions

  15. Example • Default agent can’t cross the street. • How an agent crosses the street… • Enters a Crosswalk Situation • Crosswalk situation extends agent • Sensor to see traffic light • Behavior Functions to cross street • Behavior Functions to stop • Rules to wait for light to change • Remove extensions when done

  16. Composing Behaviors:Action Selection Left ? Agent Right Straight

  17. Composing Behaviors:Probability Scheme Behavior Function A Left .5 Agent Right .3 Straight .2

  18. Composing Behaviors:Probability Scheme Behavior Function A Behavior Function B .4 Left .5 .1 Agent .25 Right .3 .1 .33 Straight .2 .2

  19. Composing Behaviors:Extending Agents Behav Func A Behav Func B Behav Func J Left .5 .1 .5 .41 Agent Right .3 .1 .5 .24 Straight .2 .2 .5 .33 Jump .2 .03

  20. Composing Behaviors:Probability Scheme • Simple example • Three rooms with different set of composing behaviors.

  21. Situations Compose • Agent can be in multiple situations • Agent has union of all the things that different situations put in • Details in paper

  22. Authoring: Painting interface • Author environments (not characters) • Set of situation types • Paint into environments • Mix situations to make complex/compound ones • Ulicny et al (SCA 2004) • Painting on people, not environment

  23. Advantages • Scalability / Efficiency • Agent complexity is independent of overall world complexity • Agent only carries information for current situations • Authorability • Re-use situations • Compose / combine / paint • Stochastic control • variability

  24. Demos : Street scene

  25. Demos : Gallery scene

  26. Limitations/Future work • Behavior depends on available actions • All behaviors are concatenation of actions • Time scale issues / long term behaviors • Hierarchical / Ordered Situation • Discrete Choices • Parameterized actions? • Aggregate Control (e.g. Density) • Probability Tuning?

  27. Summary:Scalable Crowd Simulation • Situation-Based Approach • Simple Extensible Agents • Localized behaviors • Behavior decomposed in situations • Situation Composition • Probability distributions

  28. Acknowledgements • Game Engine Integration: Aaron San Filippo • Financial support : NSF CCR-9984506 and CCR-0204372, MIC of Korea • Motion donations : House of Moves, Demian Gordon, Ohio State University • UW Graphics Group, especially Hyun Joon Shin and Lucas Kovar

  29. Back up slides

  30. Performance evaluation

  31. Performance evaluation

More Related