What is Game AI?Final Thoughts

What is Game AI?

• Game AI is not about :

‣ Beating the player

‣Optimality

‣ “making the bad guys shoot”

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What is Game AI

• Game AI is about :

‣Delivering great experiences to players

‣ Building immersive environments

‣ Automating the functions of a human “Dungeon Master”

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Batman - Arkham Asylum

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Batman - Arkham Asylum

• A classic example of the game AI challenge

•We could coordinate the thugs, use small unit

tactics etc.

• Make use of existing AI techniques to make

intelligent, successful thugs.

• Is this the point?

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Batman - Arkham Asylum

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Batman - Arkham Asylum

• The overall aim is to make a Batman “simulator”.

• Make the player feel like they ARE Batman.

• Recall “Losing with Style”

‣ Epic brawls

‣ Batman can generally win if he’s smart

• Player is entertained and immersed in the world of

Batman.

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The “Odd Sock Drawer”

Stuff That Didn’t Fit InAnywhere Else

AKA “The Brain Dump”

Emergence

• Emergence is the term for “interesting” things

coming from seemingly simple rules.

• Recall L-Systems from last week

‣ Basic rules : draw, turn left, turn right

‣ Complicated patterns emerged from the interaction of

these rules

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Reynolds Steering

• Three simple rules for steering

‣ Separation - avoid crowding local units

‣ Alignment - steer towards the avg heading of local units

‣ Cohesion - steer towards the avg position of local units

• From these three rules we can simulate “flock”

behaviour

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Boids

• Demo - http://lukedicken.com/projects/AITest/

WebPlayer.html

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Cellular Automata

• A CA is a grid representation

• Individual cells are “off” or “on”

‣ Actually can take a range of values, but usually binary.

• Iterative process to determine at a given step a cell’s

state

• Most famous ruleset is Conway’s Game of Life

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Conway’s Game of Life

• Under-population : Any live cell with fewer than

two live neighbours dies

•Overcrowding : Any live cell with more than three

live neighbours dies

• Reproduction : Any dead cell with exactly three live

neighbours becomes a live cell

• Live cells with two or three live neighbours lives

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Gosper’s Glider Gun

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The Glider

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CA and the Universe

• There are some people who believe that CA

emergence reflects the nature of our universe.

• The universe is claimed to be a Turing Machine.

• Through the property of Turing Completeness any

Turing Machine can replicate behaviour of another.

• CAs are a Turing Machine.

‣ Some researchers claim to be discovering laws of our

universe by analysis of CA systems16

CAs for Games

• CAs are of interest in the context of Game AI

• Can be used to generate “particles”

‣ Think confetti

• Particle systems are used for all sorts in graphics

‣ Smoke, vapour, dust, leaves, sparkles, explosions, flames

• Rather than defining a complex particle system

• Use a lightweight CA definition and a start state

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Emergence

• Emergence allows us to define complex systems

simply.

• Diverse behaviour can be exhibited

‣Novel, unexpected behaviour

• Loss of directorial control when AI becomes

unpredictable.

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Traditional vs Game AI

• Something I hear on a fairly regular basis is how bad

AI in games is.

• “It’s not intelligent”

• “You could do this with <insert algorithm>”

•We’ve talked at length about the different

motivations of Traditional AI and Game AI

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Processing Time

•One of the big things that is different between is the

amount of CPU power available.

•When we talk about traditional AI systems, that’s

typically all that the computer is doing.

•Our AI systems need horsepower.

• Games need horsepower too.

‣ Graphics, physics, networking.

‣ 1ms of CPU time per frame - <10% of time given to AI20

Memory Usage

• Memory is another big factor.

• Consider the kind of search trees we’ve been

talking about.

‣ Combinatorial explosion

‣ Tracking massive amounts of states

• Memory required for non-trivial planning quickly

hits multiple gigabytes.

‣ For one planning problem21

Debugging AI Systems

• Lots of our AI systems are randomised

• How can we accurately test behaviour if it relies on

random chance?

•We talked previously in Poker context about how

to overcome randomness

‣Many repetitions to minimise variance

‣ Find a way to rig the randomness

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Pseudo-Random Behaviour

•We know that computers cannot generate truly

random numbers.

•What they actually use are complicated functions

using a “seed” number to initialise them.

•We can generate the same number sequence iff

‣We know the function

‣We know the seed

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Car Key

• At a very basic level, this is how many cryptography

techniques work.

• Consider a remote car key

• Lots of keys share the same frequency

‣Need to distinguish our key/car pair

• Thieves could intercept the unlock command if it

was broadcast in the clear.

‣ Replay Attack24

Rolling Code

• Get around this using a pair of pseudo-random

number generators between lock and key.

‣ Synchronised by having the same seed number

• Both lock and key know what the next number in

the sequence is.

• If a thief intercepts it, that code is now “old”

‣Need to be able to predict what the next number is

‣ Lots of extra stuff to make this difficult/impossible25

Debugging AI Systems

•We can use the same basic principles to ensure that

when we debug an AI system, we are observing the

same decisions.

• Crash reports always report seed number used.

•We can replicate the random number sequence that

was generated before the crash

‣ Replicate the behaviour that caused the crash

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A Note on Footprints

• Activating debug code and changing your program

can alter it’s footprint when compiled.

• Can lead to unintentional changes in behaviour.

‣Debug build takes longer to execute as there are more

instructions - different amounts of processor available,

behaves differently

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Software Engineering for (Game) AI

• By now you will have been told about best practices

for Software Engineering.

‣ UML diagrams

‣Waterfall method

• Good for well understood tasks

‣Write an app that does this

•What if the task isn’t understood?

‣ How can we design a system on paper if it isn’t specified?

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Iteration, Iteration, Iteration

• The way we deal with not knowing the specification

in advance is to constantly test.

• Rapid iteration

‣ Building systems incrementally

‣No monolithic approach

‣Make it do something, make it do the right thing later

‣ Iterative refinement

• Testing code correctness every few lines.

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Reality

• Continuous Integration

‣ Code being committed is going through automated tests

to ensure correctness

• Long build processes

• Core reason a lot of AI is based on scripting

‣No need to alter the code, no need to wait for a

recompile.

‣ Script updates can be executed on the same build.

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Scrum

• Scrum is a project management technique getting a

lot of use in development teams

• Growing in popularity since 2001, although dates

back to 1986

• Tightly integrates a product-centric view of the

development process

‣ Avoids teams working on “cool” rather than “useful”

features.31

Roles

• Product Owner - Represents the “product”, client’s

perspective. Ensures that the team is providing

value.

• Scrum Master - In charge of ensuring smooth

operation of the team. Not leader of the team.

‣ Somewhere between Mum and Fixer

• Team - Developers

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The Product Backlog

• Created by the Product Owner

• Prioritised list of potential features.

‣ Priority based on value of feature and work involved

• Product Owner and Team determine priority

‣ Value is set by the Product Owner

‣ Amount of work is set by the team

• Items in the Product Backlog must be promoted to

the Sprint Backlog before being worked on.33

The Sprint

• Building block of Scrum

• A development process consists of multiple sprints

‣ Each sprint lasts between a week and a month

‣ Firm deadline for the length of the sprints

• Sprint Planning Meeting selects what items from the

Product Backlog are going to be tackled.

• Sprint Review Meeting analyses what has/hasn’t

been accomplished at the end. Demo.34

The Scrum

• Daily Team meeting - 15m

‣What have you done since yesterday?

‣What are you going to do today?

‣ Are there any obstacles right now?

• Scrum of Scrums

‣Daily summary

‣ Each team sends a delegate

‣ Allows inter-team communication and progress checking

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Maslow’s Hammer

• “If all you have is a hammer, everything looks like a

nail” - The Psychology of Science, Maslow

• This plagues the AI field - especially in academia

• Experts in one particular aspect

‣Or grads who learnt about one technique/algorithm

• Go on to use it as standard approach everywhere,

even when it’s not at all appropriate.

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The Philosophy of this Module

• As much as possible I’ve avoided talking about

specific algorithms.

• Algorithms are available in books or on Wikipedia

•What I’ve tried to emphasise is approaches and

application areas.

• Teaching ways of thinking about Game AI

‣Not how to write Game AI

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The AI Toolbox

• Different techniques are suited to different jobs.

•Whenever you come across a new technique, make

a note of it.

‣ Add it to your toolbox

•When you come across a new problem :

‣Do you have a tool that can solve it?

‣ Is there a better one available?

• These lectures hopefully give you a “starter kit”.38

Final Summary

• Science of playing games

• Building mathematical representations of players.

• Generating content for games

• Tailoring content to players

• Managing the experience of players

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Source Material

• Largely drawn from articles I’ve written for

‣ AIGameDev.com

‣ AltDevBlogADay.com

‣ Gamasutra.com

•Other aspects based on a series of posts

forthcoming for Gamasutra

• Also based on conversations with / talks from the

following people (and more) over the past few years40

Acknowledgements

• Phil Carlisle (Namaste)

• Alex Champandard (AIGameDev.com)

• Kevin Dill (Lockheed Martin Advanced Simulation Center)

• Richard Evans (Stumptown Game Machine)

• Dan Kline (Electronic Arts - Maxis)

• Dave Mark (Intrinsic Algorithm, Game AI Programmers Guild)

• Gwaredd Mountain (Climax Studios)

• Brian Schwab (Blizzard Entertainment)

• Togelius and Yannakakis (ITU Copenhagen)41

Finally

• Strathclyde AI and Games research group

‣ Talk to us about postgrad opportunities

• International Game Developers Association

‣ IGDA Scotland

‣ IGDA Scholarships

•Organising some form of game-development based

program here at Strathclyde

‣ Keep an eye on your email in the next week or two

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