practical ai in games

PRACTICAL AI IN GAMES

Renaldas Zioma Unity Technologies

(INTRO LEVEL)

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INTRO• Why graphics programmer is talking about AI?

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NO AI IN GAMES!• No “intelligence”

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• This statement is 99% true

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• This statement is 99% true • Behaviors are predefined - game does not learn!

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• This statement is 99% true • Behaviors are predefined - game does not learn!

• … but going to change soon-ish

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• Hard!

Chess• Average branching factor = 35• Average depth = 80• Search space = 1080.

Go• Branching factor = 250• Average depth = 150

• Search space = 10360

• Branching factor = 3050 to 30200 !!!• Average Depth = ???• Search space =

Starcraft

💣 💣 💣

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AND NOT REALLY THE POINT!• AI is not to “solve” your game

• Entertain player

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DESIGN PRINCIPLES• Fun - instead of “just hard”

• Competence

• Autonomy

• Relatedness

• Immersion

• Presence

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• Responsive - because slow feels “stupid”

DESIGN PRINCIPLES

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DESIGN PRINCIPLES

• Relatable - if player does not understand that NPC is “smart”, you just wasted your time

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DESIGN PRINCIPLES• Fun - instead of “just hard”

• Responsive - slow feels “stupid”

• Relatable - if player does not understand that NPC is “smart”, you just wasted your time

• Player should feel “smart” - not AI or you!

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DESIGN PRINCIPLES• Fun, Responsive, Relatable

• Player should feel “smart”

• AI is a tool for game designer!

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PERFORMANCE

• You have 3 ms for AI !!!

• Graphics, physics, audio - first, because crucial for immersion • GPU does NOT do all graphics work!

• AI usually is time sliced - executed over several frames

1 second = 1000 ms

30 FPS = 1000 / 30 = ~33 ms

60 FPS = 1000 / 60 = ~16 ms

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AI “STACK”• Example from Robotics • Autonomous Car Architecture

Navigation

Behavior Planner

Driver

Motion Planner

Trajectory Planner

Nicolas MeuleauUnity Technologies

exApple, exNASADirector of AI Research

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AI “STACK”• Games

Navigation

Animation controller

Trajectory, Steering

Behavior

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NAVIGATION = PATHFINDING• Grid • Navigation Mesh (“NavMesh”) • Waypoint Graph

Navigation

Trajectory

Behavior

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NAVMESH

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WAYPOINT GRAPH

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GRID• Pros

• easy • strategy games

• Cons • flat • dense, takes memory

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NAVMESH• Pros

• 3D • average memory footprint • many genres

• Cons • hard to construct

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WAYPOINT GRAPH• Pros

• designer control • low memory footprint

• Cons • manual work

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COMMON PROBLEMS• Static world

• dynamic obstacles are easier to support with grid

• Actor size is predefined

• Fast moving actors with steering constraints • wheeled vehicles

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COMMON SOLUTIONS• Delegate solution to lower-level systems

• steering for dynamic obstacle avoidance

• Several data structures • different data structures can co-exist • several copies with different parameters

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STEERING

Navigation

Trajectory, Steering

Behavior

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STEERING• Path simplification

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STEERING• Path simplification

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• Path simplification • Dynamic object avoidance

STEERING

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STEERING

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BEHAVIOR

Navigation

Trajectory

Behavior

• State Machine • Behavior Tree • Utility AI

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FINITE STATE MACHINE = FSM

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FINITE STATE MACHINE

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public class AttackBehaviour : StateMachineBehaviour{

public float power = 1.0f;public GameObject particle;private GameObject newParticle;override public void OnStateEnter(Animator animator, AnimatorStateInfo stateInfo, int layerIndex){

newParticle = Instantiate(particle, …Rigidbody particlePhysicalBody = newParticle.GetComponent<Rigidbody>();particlePhysicalBody.AddExplosionForce(power, animator.rootPosition, …

}override public void OnStateExit(Animator animator, AnimatorStateInfo stateInfo, int layerIndex){

Destroy(newParticle);}override public void OnStateUpdate(Animator animator, AnimatorStateInfo stateInfo, int layerIndex){

Debug.Log(“Attacking every frame!!!");}

// . . .

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• Pros • easy • visual

• Cons • complex behavior gets messy!

• Hierarchical FSM helps • hardcoded decisions

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PACMAN

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PACMAN

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PACMAN

Navigation

Animation

Steering

Behavior

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PACMAN• I felt it would be too stressful for a human […] to be continually surrounded and

hunted down. So I created the monsters’ invasions to come in waves. They’d attack and then they’d retreat. As time went by they would regroup, attack, and disperse again. It seemed more natural than having constant attack.

- Toru Iwatani, Pac-Man creator

• Just before ghosts turn a corner, their eyes move to point in the new direction of movement

- Ghost Pshychology, www.webpacman.com

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PACMAN

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•追いかけ, oikake - “chaser” •待ち伏せ, machibuse -“ambusher” •気紛れ, kimagure - “moody” •お惚け, otoboke - “feigning ignorance”

PACMAN

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GHOST STATEMACHINE

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PACMAN

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BEHAVIOR TREE = BT

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BEHAVIOR TREE• Selector • Sequence • Action

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BEHAVIOR TREE

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BEHAVIOR TREE• Pros

• better at complexity than FSM • used in many games: Halo, Bioshock, Crysis, Spore

• Cons • large BT can get slow • hardcoded decisions

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UTILITY AI

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UTILITY AI

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UTILITY AI

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UTILITY AI

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UTILITY AI• Pros

• can achieve “smarter” behavior than BT • Killzone, Sims, Civilization

• Cons • can be hard to design scorers and debug • some loss of designer control • still pretty hardcoded logic

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FSM / BT / UTILITY HOW TO CHOOSE?• Pick approach you are most comfortable with

• Pick the simplest tool that will make job done

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PLAN AHEAD

• FSM, BT, Utility AI - reactive • only reacts to current situation • no planning

• Deliberate AI • plans ahead

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INFLUENCE MAPS• Add Behavior related

information to path planning

• Simple way to plan for actions ahead • piggy-back pathfinding code

• Better decisions

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INFLUENCE MAPS• Types of data:

• covers • distance to enemies • distance to power-ups • heatmaps • etc…

FUTURE

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FUTURE• Intelligence in AI • Bluffing • Learning

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FUTURE• More Planning ahead • Machine Learning

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PLANNING IN GAME AI• Goal Oriented Action Planning

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GOAP• Games: F.E.A.R, S.T.A.L.K.E.R, Fallout 3, Just Cause 2

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GOAP• Pros

• better decisions • emergent gameplay

• Cons • performance! worse than NP-hard! • loss of designer control • so far didn’t became common approach

• maybe needs better tools

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BIOLOGICAL BRAIN• Consists of Neurons

• Ultra low power consumption

• Both DIGITAL and ANALOG!

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NEURON• Can compute • Can learn • Can generate patterns

• Form networks - highly interconnected • Very parallel - but single one is relatively slow

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NEURON• Both digital and analog! • Working principle includes 3 things at once:

• Electrical • Bio-mechanical • Chemical

• IMHO in the 21st century you must study some neuroscience! DO IT!

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NEURON, APPROXIMATELY• Sums up weighted inputs • Makes binary decision • Sends to connected neurons

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HARDWARE

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TPU• Tensor Processing Unit • Google

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TPU• Server running AlphaGO • Won against Lee Sedol

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FPGA• Field Programmable Gate Array

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DATA FLOW PROCESSORS• Some prototypes - neuFlow

• Used for Convolutional Networks for vision

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NEUROMORPHIC CHIPS• IBM TrueNorth

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NEUROMORPHIC CHIPS

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TRUE NORTH

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CEREBRAL ORGANOIDS• Madeline Lancaster, Cambridge MRC Lab

• since 2014 • Small artificially grown biological brains!

• ~4 million neurons

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CEREBRAL ORGANOIDS• Easier way to study and learn from real “intelligence”

• Eventually will be connected to computers • biological AI co-processor

The Summer of Deep Learning

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BACK TO GAME AI• Machine Learning is more like Intuition

• Planning is more like Reason

• Need both

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