Scalable Behaviors for Crowd Simulation Mankyu Sung Michael Gleicher Stephen Chenney University of Wisconsin- Madison

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

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

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

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

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

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 In front of Store Window Possibly: stop to window shop Friends Together: Possibly: Stop to talk Probably: Have same goal Doorway In front of Doorway Possibly: open door, enter Unlikely: stand blocking door Use crosswalk: Wait for green light Start crossing Sidewalk: Walk here

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

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

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 patterns but…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 actions but… difficult to compose or get variability

Related Work Inspirations 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, …

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

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

Situation-Based Approach: Extensible Agent Situations extend agents Add Actions Add Behavior Functions Add Sensors and Rules that inform Behavior Functions

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

Composing Behaviors: Action Selection Agent Left Right Straight ?

Composing Behaviors: Probability Scheme Behavior Function A Agent Left Right Straight.5.3.2

Composing Behaviors: Probability Scheme Behavior Function A Agent Left Right Straight Behavior Function B

Composing Behaviors: Extending Agents Agent Left Right Straight Behav Func A Jump Behav Func B.1.2 Behav Func J

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

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

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

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

Demos : Street scene

Demos : Gallery scene

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?

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

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

Back up slides

Performance evaluation