Stanford 4 00

Autonomous Charactersfor Games and Animation: 

Autonomous Characters for Games and Animation Craig W. Reynolds Sony Computer Entertainment America May 1, 2000

Autonomous Charactersfor Games and Animation: 

Autonomous Characters for Games and Animation Self-directing characters which operate autonomously ('puppets that pull their own strings' -Ann Marion) Applications in: games and other interactive venues animation for television and feature films History: first used experimentally in 1987 in wide commercial use today

Autonomous Characters: 

Autonomous Characters Autonomous agents for simulated 3D worlds situated embodied Intersection of several fields ethology artificial life autonomous robotics dramatic characters Adjunct to physically-based modeling dynamics versus volition bouncing ball versus pursuing puppy

Reactive Behavior: 

Reactive Behavior Behavior driven by reaction to environment both passive scenery and active characters Simplifies complex animation many characters can be animated by a single behavior Allows user interaction improvisational style permits unscripted action

Creating Character Behaviors: 

Creating Character Behaviors By design programming authoring (example: Motion Factory) Through self-organization evolution and other forms of machine learning: neural nets decision trees classifier systems simulated annealing

Ad hoc Behavioral Hierarchy: 

Ad hoc Behavioral Hierarchy Action selection goals and strategies Path selection / steering global motion Pose selection / locomotion local motion (animation)

Behavioral Blending: 

Behavioral Blending Discrete selection One behavior at a time Behavioral blending Summation / averaging Per 'frame' selection (blend via inertia) First active Stochastic (dithered) decision tree

Behavioral Animation: 

Behavioral Animation

Behavioral Animation: 

Behavioral Animation Background action Autonomous characters behavioral model graphical model Improvised action

Behavioral Animation:Group Motion: 

Behavioral Animation: Group Motion Individual simple local behavior interaction with: nearby individuals local environment Group: complex global behavior

Behavioral Animation:Examples of Group Motion: 

Behavioral Animation: Examples of Group Motion People crowds, mobs, passersby Animal flocks, schools, herds Vehicle traffic

Applications of Behavioral Animations: 

Applications of Behavioral Animations 1987: Stanley and Stella in: Breaking the Ice, (short) Director: Larry Malone, Producer: Symbolics, Inc. 1988: Behave, (short) Produced and directed by Rebecca Allen 1989: The Little Death, (short) Director: Matt Elson, Producer: Symbolics, Inc. 1992: Batman Returns, (feature) Director: Tim Burton, Producer: Warner Brothers 1993: Cliffhanger, (feature) Director: Renny Harlin, Producer: Carolco. 1994: The Lion King, (feature) Director: Allers / Minkoff, Producer: Disney.

Applications of Behavioral Animations: 

Applications of Behavioral Animations 1996: From Dusk Till Dawn, (feature) Director: Robert Rodriguez, Producer: Miramax 1996: The Hunchback of Notre Dame, (feature) Director: Trousdale / Wise, Producer: Disney. 1997: Hercules, (feature) Director: Clements / Musker, Producer: Disney. 1997: Spawn, (feature) Director: Dipp₫, Producer: Disney. 1997: Starship Troopers, (feature) Director: Verhoeven, Producer: Tristar Pictures. 1998: Mulan, (feature) Director: Bancroft/Cook, Producer: Disney.

Applications of Behavioral Animations: 

Applications of Behavioral Animations 1998: Antz, (feature) Director: Darnell/Guterman/Johnson, Producer: DreamWorks/PDI. 1998: A Bugs Life, (feature) Director: Lasseter/Stanton, Producer: Disney/Pixar. 1998: The Prince of Egypt, (feature) Director: Chapman/Hickner/Wells, Producer: DreamWorks. 1999: Star Wars: Episode I--The Phantom Menace, (feature) Director: Lucas, Producer: Lucasfilm. 2000: Lord of the Rings: the Fellowship of the Ring (feature) Director: Jackson, Producer: New Line Cinema.

Steering Behaviors: 

Steering Behaviors

Steering Behaviors: 

Steering Behaviors seek or flee from a location pursuit and evasion arrival (position / velocity / time constraints) obstacle avoidance / containment path / wall / flow field following group behaviors unaligned collision avoidance Leader following flocking (three components)

Steering Behaviors: 

Steering Behaviors ?steering behavior demos?

Boids: 

Boids

Boid Flocking (three component steering behaviors): 

Boid Flocking (three component steering behaviors) Separation steer to move away from nearby flockmates Alignment steer toward average heading of nearby flockmates (accelerate to match average velocity of nearby flockmates) Cohesion steer towards average position of nearby flockmates

Boids:Separation: 

Boids: Separation

Boids:Alignment: 

Boids: Alignment

Boids:Aggregation: 

Boids: Aggregation

Boids (full behavioral model): 

Boids (full behavioral model) Obstacle avoidance Flocking separation alignment cohesion Migratory (attraction / repulsion)

Boids Web Page: 

Boids Web Page http://www.red.com/cwr/boids.html

Boids Video: 

Boids Video ?boids video...

Boids (real time (60Hz) and interactive): 

Boids (real time (60Hz) and interactive)

Evolution of Behavior: 

Evolution of Behavior

Evolution of Behavior: 

Evolution of Behavior Agent in simulated world Evolution of behavioral controller agent morphology (see Sims SIGGRAPH 94) Fitness based on agent’s performance objective fitness metric competitive fitness

Corridor Following: 

Corridor Following

Evolution of Corridor Following Behavior in a Noisy World: 

Evolution of Corridor Following Behavior in a Noisy World Evolve controller for abstract vehicle Task: corridor following noisy range sensors noisy steering mechanism Evolution of sensor morphology

Corridor Following: goal: 

Corridor Following: goal

Corridor following: fitness: 

Corridor following: fitness

Corridor Following: Results: 

Corridor Following: Results Works well Difficulty strongly related to the representation used 'Competent' controllers easy to find Reliability of controllers is difficult to measure

Coevolution of Tag Players: 

Coevolution of Tag Players

Coevolution of Tag Players: 

Coevolution of Tag Players The game of tag symmetrical pursuit and evasion role reversal Goal: discover steering behavior for tag Method: emergence of behavior coevolution competitive fitness Self-organization: no expert knowledge required

Competition, Coevolution and the Game of Tag (ALife IV, 1994): 

Competition, Coevolution and the Game of Tag (ALife IV, 1994)

Coevolution of Taggers Revisited: 

Coevolution of Taggers Revisited December 1999 to present Similar to 1994 work, but: longer games (25®150) steering angle limits obstacles and sensors demes and species improved performance (faster computers, compilation of evolved programs)

Evolved Taggers: Obstacles and Sensors: 

Evolved Taggers: Obstacles and Sensors

Evolved TaggersDemes and Species: 

Evolved Taggers Demes and Species Species Deme migration competition

Evolved Taggers:Quality of play over time: 

Evolved Taggers: Quality of play over time

Evolved Taggers:Handmade program in the open: 

Evolved Taggers: Handmade program in the open

Evolved Taggers:Handmade among obstacles: 

Evolved Taggers: Handmade among obstacles

Evolved Taggers:Typical competitive fitness test: 

Evolved Taggers: Typical competitive fitness test

Evolved Taggers:Typical competitive fitness test : 

Evolved Taggers: Typical competitive fitness test

Coevolution of Tag Players: Results: 

Coevolution of Tag Players: Results It works! (after a lot of tweaking) An ecology of competing behaviors did arise Originally, evolved behaviors had been sub-optimal (perhaps do to collusion: 'live and let live') Finally (after demes, species, and harsh penalties) the evolved tag players have exceeded the quality of play of my hand-crafted player.

Conclusion: 

Conclusion Autonomous characters: add richness and complexity to virtual worlds automate creation of groups and crowd scenes allow life-like improvisational action can react to unanticipated situations, like user input Games and animation provide many applications of, testbeds for, and problems to be solved by research in: artificial life artificial intelligence evolutionary computation and other biologically-inspired methods

Slide47: 

Slides temporarily removed: 

Slides temporarily removed Slides temporarily removed

Applications of Autonomous Characters: 

Applications of Autonomous Characters Behavioral animation (film and television) coordinated group motion extras / background action Interactive multimedia (games / virtual reality) opponents and allies background characters Autonomous robotics search / exploration / mapping prototyping for evolutionary robotics Theoretical biology testing theories of emergent natural behavior

Combining Simultaneous Behaviors: 

Combining Simultaneous Behaviors Combination discrete selection behavioral blending Low priority behavior should not be: completely locked out allowed to contradict (and perhaps cancel out) a higher priority behavior

Autonomous Character Case Studies: 

Autonomous Character Case Studies Hand programmed steering behavior library boids hockey players Evolution corridor following tag players

Steering-BasedHockey Simulation: 

Steering-Based Hockey Simulation

Basic Hockey Player: 

Basic Hockey Player Physical model point mass limited force and velocity collision modeling (as cylinder) Awareness of position and velocity of players and puck position of rink and markings Behaviors: avoid rink walls and goal nets chase loose puck, skate towards location? Assigned role (forward, wing, defenseman, goalie)

Hockey Role Model: 

Hockey Role Model Defenseman if you have the puck? if your teammate has the puck... if puck is within your zone: - discourage shot on goal - discourage pass to opponent - don't crowd goalie do basic hockey play stuff

Hockey Demo: 

Hockey Demo ?hockey demo?

Corridor Following: Experimental Design: 

Corridor Following: Experimental Design Vehicle model constant speed limited steering angle noisy sensors (arbitrary number andamp; direction) noisy steering mechanism Genetic Programming hybrid steady-state model worst of four noisy trials population: 2000 size limit for evolved programs: 50