Advanced Graphics Computer Animation

1. Advanced GraphicsComputer Animation Autonomous Agents Spring 2002 Professor Brogan

2. Quick Reminder • Assignment 1 “take away message” • It’s hard to build intuitive interfaces • Adding knots to spline (beginning, middle, end) • Graphics makes it easy to add feedback that lets the userdecide how to accomplish tasks • Highlight potential objects of an action before execution and change their graphical state once again when an action is initiated

3. Autonomous Agents • Particles in simulation are dumb • Make them smart • Let them add forces • Virtual jet packs • Let them select destinations • Let them select neighbors • Give them artificial intelligence

4. History of AI / Autonomous Agents – Cliff Note Version • 1950s – Newell, Simon, and McCarthy • General Problem Solver (GPS) • Use means-ends analysis • Subdivide problem • Use transformation functions (actions) to subdivide and solve subtasks • Useful for well-defined tasks • Theorem proving, word problems, chess • Iteration and exhaustive search used

5. History of AI • 1960s – ELIZA, chess, natural language processing, neural networks (birth and death), numerical integration • 1970-80s – Expert systems, fuzzy logic, mobile robots, backpropagation networks • Use “massive” storage capabilities of computers to store thousands of “rules” • Continuous (not discrete) inputs • Multi-layer neural networks

6. History of AI • 1990s - Major advances in all areas of AI • machine learning • intelligent tutoring • case-based reasoning • multi-agent planning • scheduling • data mining • natural language understanding and translation • vision • games

7. So Many Choices • Important factors: f(state, actions)=state_new • # of inputs(state) and outputs(actions) • Previous states don’t matter (Markov) • Actions are orthogonal • Continuous versus discrete variables • Differentiability of f( ) • Model of f( ) • Costs of actions

8. Example: Path Planning • Important factors: f(state, actions)=state_new • # of inputs(state) and outputs(actions) • Previous states don’t matter (Markov) • Actions are orthogonal • Continuous versus discrete variables • Differentiability of f( ) • Model of f( ) • Costs of actions • State • Position, velocity,obstacle positions,goal (hunger, mood,motivations), what you’ve tried • Actions • Movement (joint torques), get in car, eat, think, select new goal

9. Path Planning • Do previous states matter? • Going in circles • Are actions orthogonal? • Satisfy multiple goals with one action • Continuous versus discrete? • Differentiability of f( ) • If f(state, action1) = state_new_1, doesf(state, 1.1 * action1) = 1.1 * state_new_1?

10. Path Planning • Model of f( ) • Do you know the result of f(s, a) before you execute it? • Compare path planning in a dark, unknown room to path planning in a room with a map • Costs of actions • If many actions take state -> new_state • How do you pick just one?

11. Let’s Keep it Simple • Make particles that can intelligently navigate through a room with obstacles • Each particle has a jet pack • Jet pack can swivel (yaw torque) • Jet pack can propel forward (forward thrust) • Previous states don’t matter

12. Particle Navigation • State = position, velocity, obstacle positions • Action = sequence of n torques and forces • Solve for action s.t. f(s, a) = goal position • Minimize sum of torques/forces (absolute value) • We have a model: f=ma • Previous states don’t matter • We don’t care how we got to where we are now • Tough problem • Lots of torques and forces to compute • Obstacle positions could move and force us to recompute

13. Simplify Particle Navigation • State = position, velocity, obstacles • Action = torque, force • F (s, a) = new position, velocity • Find action s.t. position is closer to goal position • Smaller action space • Local search – could get caught in local min (box canyon) • Adapts to moving obstacles

14. Multiple Particle Path Planning • Flocking behavior • Select an action for each particle in flock • Avoid collisions with each other • Avoid collisions with environment • Don’t stray from flock • Craig Reynolds: Flocks, Herds, and Schools: A Distributed Behavioral Model, SIGGRAPH ’87

15. Flocking • Action choices

16. Models to the Rescue • Do you expect your neighbor to behave a certain way? • You have a model of its actions • You can act independently, yet coordinate

17. The Three Rules of Flocking • Go the same speed as neighbors (velocity matching) • Minimizes chance of collision • Move away from neighbors that are too close • Move towards neighbors that are too far away

18. Emergent Behaviors • Combination of three flocking rules results in emergence of fluid group movements • Emergent behavior • Behaviors that aren’t explicitly programmed into individual agent rules • Ants, bees, schooling fishes

19. Local Perception • Success of flocking depends on local perception (usually considered a weakness) • Border conditions (like cloth) • Flock splitting

20. Ethological Motivation • ethology: the scientific and objective study of animal behavior especially under natural conditions • Perception (find neighbors) and action • Fish data

21. Combining three rules • Averaging desired actions of three rules can be bad • Turn left + Turn right = go straight… • Force is allocated to rules according to priority • First collision detection gets all it needs • Then velocity matching • Then flock centering • Starvation is possible

22. Action Selection • Potential Fields – Collision Avoidance

23. Scaling Particles to Other Systems • Silas T. Dog, Bruce Blumberg, MIT AI Lab • Many more behaviors and actions • Internal state • Multiple goals • Many ways to move

24. Layering Control • Perceive world • Is there food here? • Strategize goal(s) • Get food • Compute a sequence of actions that will accomplish goal(s) • Must walk around obstacle • Convert each action into motor control • Run, gallop, trot around obstacle

25. Multiple Goals • Must assign a priority to goals • Can’t eat and sleep at same time • Can’t dither back and forth between goals • Don’t start eating until finished sleeping • Don’t let goals wither on priority queue • Beware of starvation (literally) • Unrelated goals can be overlapped • Eating while resting is possible