A rtificial I ntelligence for Games

Artificial Intelligence for Games Minor Games Programming Lecture 1

Artificial Intelligence for Games • Introduction to game AI (self study) • Theory: Moving Game Agents Jan Verhoeven j.verhoeven@windesheim.nl

Game AI • Game AI is not a subset of AI • Game AI • often covers techniques that are not considered “AI-like” • AI • uses techniques impractical in a game context AI Game AI

What is Game AI? • Analogy • game AI is to "real" AI as • stage design is to architecture • The goal of game AI is to give the impression of intelligence • to avoid the impression of stupidity • to provide a reasonable challenge for the player

Challenge • It is very possible to make the computer too smart • think: driving game or a chess game • The task of AI is to support the experience • many compromises from “optimal” required

Not dumb • It is surprisingly hard to make the computer not dumb • “Why are computers so stupid?” • especially with limited computational resources • Example • Humans are good at navigating complex 3-D environments • Doing this efficiently is (still) an unsolved problem in AI

But • Game AI is the future of games • Many designers see AI as a key limitation • the inability to model and use emotion • the inability of games to adapt to user’s abilities • the need for level designers to supply detailed guidance to game characters

Study book Literature: Mat Buckland Programming Game AI by Example http://www.ai-junkie.com

What we will cover • Finite-state machines (Previous Knowledge !!) • the most basic technique for implementing game AI • fundamental to everything else • Steering behaviors • basic behaviors for avoiding stupidity while navigating the world • Path planning • the surprisingly tricky problem of getting from point A to point B • Action planning • assembling sequences of actions • Fuzzy logic • reasoning by degrees

Today's Theory: Moving Game Agents,(see study book: chapter 3) • What is an Autonomous Agent? • Steering Behaviors • Group Behaviors • Combining Steering Behaviors • Spatial Partitioning • Smoothing

Movement • Two types • Reactive movement • Planned movement

Steering behaviors • Tendencies of motion • that produce useful (interesting, plausible, etc.) navigation activity • by purely reactive means • without extensive prediction • Pioneering paper • Reynolds,1999

Examples • I want the insect monsters to swarm at the player all at once, but not get in each other's way. • I want the homing missile to track the ship and close in on it. • I want the guards to wander around, but not get too far from the treasure and not too close to each other. • I want pedestrians to cross the street, but avoid on-coming cars.

Steering behavior solution • Write a mathematical rule • that describes accelerations to be made • in response to the state of the environment • Example: "don't hit the wall" • generate a backwards force inversely proportional to the proximity of the wall • the closer you get, the more you will be pushed away • if you're going really fast, you'll get closer to the wall, but you'll slow down smoothly

Combining forces • Behaviors can be combined by • summing the forces that they produce • Example: follow • I want the spy to follow the general, but not too close • two behaviors • go to general's location • creates a force pointing in his direction • not too close • a counter-force inverse proportion to distance • where the forces balance • is where spy will tend to stay

Steering Behaviors:Physics Model • Simple Vehicle Model • orientation, mass, position, velocity • max_force, max_speed • Forward Euler Integration • steering_force = truncate (steering_dir, max_force) • acceleration = steering_force / mass • velocity = truncate (velocity + acceleration, max_speed) • position = position + velocity Read Only Page

Steering Behaviors:Seek and Flee • Seek – Steer toward goal • Flee – Steer away from goal • Steering force is the difference between current velocity and desired velocity • Blue is steering force, magenta is velocity

Steering Behaviors:Pursue and Evade • Based on underlying Seek and Flee • Pursue – Predict future interception position of target and seek that point • Evade – Use future prediction as target to flee from (Another Chase and Evade demo)

Steering Behaviors:Wander • Type of random steering with long term order • Steering is related from one frame to another • Maintains state • Red dot is wander direction • Constrained to be on black circle • Randomly moves within white circle each frame

Steering Behaviors:Arrival • Goal to arrive at target with zero velocity • Red circle is maximum distance before slowing down

Steering Behaviors:Obstacle Avoidance • White box is future path • Steering force strictly left or right • Braking force stronger as collision gets closer

Obstacle avoidance II • Basic idea • project a box forward in the direction of motion • think of the box as a "corridor of safety" • as long as there are no obstacles in the box • motion forward is safe • To do this • find all of the objects that are nearby • too expensive to check everything • ignore those that are behind you • see if any of the obstacles overlap the box • if none, charge ahead • if several, find the closest one • this is what we have to avoid

Obstacle avoidance III • Steering force • we want to turn away from the obstacle • just enough to miss it • we want to slow down • so we have time to correct • Need a steering force perpendicular*to the agent's heading • proportional to how far the obstacle protrudes into the detection box • Need a braking force anti-parallel to agent's heading • proportional to our proximity to obstacle * http://en.wikipedia.org/wiki/Perpendicular

Steering Behaviors:Hide • Hide attempts to position a vehicle so that an obstacle is always between itself and the agent (“the hunter”) it’s trying to hide from

Steering Behaviors:Wall Following • Move parallel and offset from gray areas • Goal to remain given distance from wall • Predict object’s future position (black dot) • Project future position to wall • Move out from wall set amount from normal • Seek toward new point (red circle)

Steering Behaviors:Path Following • Path is connected line segments with radius • Corrective steering only when varying off of path • Red dot future predicted position • Red circle is closest spot on path • Corrective steering toward white circle farther down path

Combined Steering Behaviors:Group Path Following • Path following with separation steering • Combined with weighted sum • Path following has 3 times weight as separation

Combined Steering Behaviors:Leader Following (Group) • Combines separation and arrival • Arrival target is a point offset slightly behind the leader • Followers must move out of leader’s future path

Combined Steering Behaviors:Leader Following (Queue) • Combines separation and arrival • Each object has a different leader

Combined Steering Behaviors:Unaligned Collision Avoidance • Objects moving in all directions (unaligned) • Combines containment and avoidance • Future collisions predicted and objects steer away from collision site, or speed-up / slow-down

Combined Steering Behaviors:Queuing • Seek doorway, Avoid gray walls, Separation from each other, Braking if others nearby or in front

Flocking

Flocking • First demonstrated by Craig Reynolds in his 1987 SIGGRAPH paper and movie • “Flocks, Herds, and Schools: A Distributed Behavior Model” • Film (Stanley and Stella in "Breaking the Ice" • Used to give flocks of birds and schools of fish eerily realistic movement • Won an Oscar in 1997 for his flocking work • (Scientific and Technical Award) • Flocking is an example of emergent behavior (a-life) • Simple individual rules result in complex group behavior • Individual creatures often called “boids” • PS2 technical demo • OpenSteer demo

Flocking:Three simple rules Separation Alignment Cohesion

Separation • "Don't crowd" • Basic idea • generate a force based on the proximity of each other agent • sum all of the vectors • Result • Each agent will move in the distance that takes it furthest from others • Neighbors disperse from each other

Alignment • "Stay in step" • Basic idea • keep an agent's heading aligned with its neighbors • calculate the average heading and go that way • Result • the group moves in the same direction

Cohesion • "Stay together" • Basic idea • opposite of separation • generate a force towards the center of mass of neighbors • Result • group stays together

Combining these behaviors • We get flocking • different weights and parameters yield different effects • animation • demo

Implementation issues • Combining behaviors • each steering behavior outputs a force • it is possible for the total force to exceed what an agent's acceleration capacity • What to do?

Combination methods • Simplest: Weighted truncated sum, • weight the behaviors, add up, and truncate at max_force • very tricky to get the weights right • must do all of the calculations • Better: Prioritization • Evaluate behaviors in a predefined order • obstacle avoidance first • wander last • Keep evaluating and adding until max_force reached • Problem is getting the fixed priority right • Cheaper: Prioritized dithering • Associate a probability with each behavior • probabilities sum to 1 • That behavior will get its force applied a certain percentage of the time

Partitioning • We want to calculate the neighbors of each agent • if we look at all agents, n2 operation • if there are many, many agents, too slow • Many techniques for speeding this up • basic idea is to consider only those agents that could be neighbors • carve up space and just look at the relevant bits • Very important in other parts of game programming, too • collision detection • view rendering

Cell-space partition • Cover space with a grid • Maintain a list of agents in each cell • not that expensive since it is just an x,y threshold test • Calculate which grid cells could contain neighbors • check only those agents in the effected cells • O(n)

Smoothing • Jitter occurs when behaviors switch in and out • obstacle avoidance kicks in when objects is in detection box • but other behaviors push back towards obstacle • Solution • average the heading over several updates Read study book for a solution or take notice of: http://blogs.msdn.com/shawnhar/archive/2007/04/23/hysteresis.aspx

Alternative to Flocking:Simple Swarms • Computationally simpler • Doesn’t enforce separation or interpenetration • Example • Hundreds of spiders crawling around, up walls, and dropping from the ceiling – Tom Scutt, Tomb Raider series

Simple Swarmsattacking Player • Outer zone • If heading toward player: waver heading • If heading away: steer toward player • Inner zone • Swirl around player • Flee after random amount of time

Swarm Intelligence • Technique based on collective behavior in decentralized, self-organized systems • Beni & Wang (1989) • Simple agents interact locally • Global behavior emerges • Ant colonies • Bird flocking • Animal herding • Swarm Robotics • Combining swarm intelligence with robotics

Formations • Mimics military formations • How is it similar to flocking? • How is it different from flocking?

Formations • Issues • Is there a leader? • Where do individuals steer towards? • What happens when they turn? • What happens when they change heading by 180 degrees? • What happens when there is a narrow pass? • Formation splitting and reforming?

A rtificial I ntelligence for Games