Game AI 101 - NPCs and Agents and Algorithms... Oh My!

Game AI 101
NPCs and Agents and
Algorithms... Oh My!

Luke Dicken
Strathclyde AI and Games Research Group
University of Strathclyde

Who Am I

• PhD Student at University of Strathclyde

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Who Am I

‣ BSc(Hons), MSc, MRes(Dist) in AI-related ﬁelds

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Who Am I

• Founding member of SAIG

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Who Am I

• AltDevBlogADay author

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Who Am I

• AltDevConf organiser

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Who Am I

• Board of Directors for IGDA Scotland

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Who Am I

• Board of Directors for IGDA Scotland
• Recipient of the 2012 Eric Dybsand Memorial AI
Scholarship to attend GDC
2

Game AI 101

• Today going to look at techniques to populate game
worlds with characters

3

Game AI 101

• This is not state of the art stuff, this is entry level

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Game AI 101

• We will be dealing with a mix of

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Game AI 101

‣ Concepts

3

Game AI 101

‣ Concepts
‣ Examples

3

Game AI 101

‣ Concepts
‣ Examples
‣ Code

3

Game AI 101

‣ Concepts
‣ Examples
‣ Code
• Assumes familiarity with Unity
3

Pure AI vs Game AI

• “Pure” AI is a scientiﬁc discipline

4

Pure AI vs Game AI

‣ Interested in creating systems as smart as possible

4

Pure AI vs Game AI

‣ Optimal solutions to real-world problems

4

Pure AI vs Game AI

• Game AI is a whole other thing

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Pure AI vs Game AI

‣ Create “realistic simulations” of intelligence

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Pure AI vs Game AI

- E.g. Believable characters

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Pure AI vs Game AI

- E.g. Believable characters
‣ Enhance the players experience

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Pure AI vs Game AI

• Pure AI for an NPC would be to make it ﬁght as
effectively as possible.

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Pure AI vs Game AI

• Is that what we actually want to achieve?

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Pure AI vs Game AI

• Game AI aims to make an immersive experience

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Pure AI vs Game AI

‣ Represent a thug character accurately

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Pure AI vs Game AI

‣ Allow the player to “become” Batman

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Pure AI vs Game AI

‣ Allow the player to “become” Batman
• Entertainment, not problem solving
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Automated Opponents

• Centuries of trying to play games with few players

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Automated Opponents

‣ Mechanical Turk claimed to be a chess player circa 1770

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Automated Opponents

‣ First video game example - Pong 1972

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Automated Opponents

‣ Ongoing commercial interest - FPS “Bots” RTS “Skirmish”

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Automated Opponents

• Emphasis now on delivering a good player
experience.

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Automated Opponents

experience.
‣ Human-like rather than “good” AI

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Automated Opponents

experience.
‣ Human-like rather than “good” AI
‣ Sub-human in order to be beatable
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Looking and Being Smart

• Two components to Game AI

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‣ Looking Smart

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‣ Looking Smart
‣ Being Smart

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‣ Looking Smart
‣ Being Smart
• Focus in this session on “Being Smart”

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‣ Looking Smart
‣ Being Smart
• Looking Smart is more of an animation / art issue

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‣ Looking Smart
‣ Being Smart
‣ Does the character “feel” intelligent

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‣ Looking Smart
‣ Being Smart
• Good decision making still required to drive this

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‣ Looking Smart
‣ Being Smart
• Good decision making still required to drive this
‣ Be smart, then trigger equivalent “look smart”
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What is
Artiﬁcial Intelligence?

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What is
• Any time a computer has to pick between two or
more choices it can be said it is acting “intelligently”

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What is
• That isn’t to say that it is inherently making good
choices.

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What is
choices.
‣ Tho neither do people...

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What is
choices.
‣ Tho neither do people...
• Typically, looking for better-than-random levels of
sophistication
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Decision Making

• Not going to get too bogged down today in
decision making process.

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Decision Making

• Science of decisions can ﬁll multiple degree-level
courses

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Decision Making

courses
• Game Theory is a good starting point to learn more

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Decision Making

courses
• Game Theory is a good starting point to learn more
‣ “Behavioral Mathematics for Game AI” Dave Mark

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Smart or Dumb?

• Partly ignoring decision making because for Game
AI, it isn’t so important for intro level.

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Smart or Dumb?

‣ Making a decision is a good start

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Smart or Dumb?

• Partly because of the entertainment issue

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Smart or Dumb?

‣ Making good decisions is irrelevant

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Smart or Dumb?

‣ We don’t want tactical genius soldiers

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Smart or Dumb?

• Partly for rapid iteration

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Smart or Dumb?

• Partly for rapid iteration
‣ Make any decision, ﬁgure out how to make it better later
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Agent-Based Systems

• Intelligent Agents are things within our problem that
are going to be trying to act intelligently.

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Agent-Based Systems

• Could be a Roomba, a warehouse robot, an
automated trip booking system or a predator drone

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Agent-Based Systems

• Agents have three important characteristics

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Agent-Based Systems

‣ Sense the environment

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Agent-Based Systems

‣ “Think” about what to do

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Agent-Based Systems

‣ “Think” about what to do
‣ Turn “thinking” into actions within the environment
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NPCs as Agents

• One of the core places we can apply “agent-based
systems” is when thinking about NPCs

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NPCs as Agents

• Each NPC is an agent.

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NPCs as Agents

‣ In some situations we may have a “General” as the agent

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NPCs as Agents

- Directing NPCs around as its way of affecting the world.

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NPCs as Agents


• In this model

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NPCs as Agents


• In this model
‣ NPCs sense the world

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NPCs as Agents


• In this model
‣ NPCs sense the world
‣ Decide what to do, then do it
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Representations

• Software Engineering buzzwords!

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Representations

• Representation internal to one NPC or shared?

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Representations

‣ Alien Hivemind - All units know what one unit knows

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Representations

- Communal blackboard!

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Representations

‣ Modern Squad - Communication between certain units

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Representations

- This was made explicit in “Enslaved Odyssey to the West”

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Representations

- Message passing between units

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Representations

‣ Medieval Guards - No widespread communication

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Representations

‣ Medieval Guards - No widespread communication
- Pure-internal representation
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Taking Our First Steps

• For this session let’s assume we have

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‣ A 3D world

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‣ A 3D world
‣ A pre-fab for an NPC

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‣ A 3D world
- Surprise! We’ll be using the Unity soldier today

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‣ A 3D world

• We’re going to work today with FPS, but the
techniques generalise ﬁne.

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‣ A 3D world

• We’re going to work today with FPS, but the
techniques generalise ﬁne.
• Examples today come primarily from Steve
Gargolinski ( http://stevegargolinski.com )
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Moving at Random

• OK, let’s do something basic

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Moving at Random

‣ Take our NPC, pick a location and go there.

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Moving at Random

‣ When he gets there, pick another location etc.

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Moving at Random

• We need to

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Moving at Random

• We need to
‣ Pick a location randomly

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Moving at Random

• We need to
‣ Be able to move the NPC about

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Moving at Random

• We need to
‣ Be able to move the NPC about
‣ Be able to detect when we get to the location

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Moving at Random

• Demo 1

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Following the Player

• Let’s do something a bit more sophisticated

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‣ Follow the player around the map

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• This time we need to

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‣ Determine position of the target

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‣ Move towards the target

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‣ Move towards the target
• Very similar to the last example, this time we have a
non-random target

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• Demo 2

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On Patrol

• Typically, we want soldiers guarding things, patrolling

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On Patrol

‣ Go to the next waypoint

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On Patrol

‣ When you get there, go to the next in the series etc

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On Patrol

• Still just an iterative improvement on previous

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On Patrol

‣ We need to track where the waypoints are

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On Patrol

‣ Which one we are near

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On Patrol

‣ Which one we are near
‣ Which comes next in a sequence

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On Patrol

• Demo 3

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Looking (Kinda) Smart

• An NPC that does just one thing is not smart

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• Raging gun battle, it doesn’t make sense to carry on
patrolling.

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• Raging gun battle, it doesn’t make sense to carry on
patrolling.
• We’ve got now three different sets of code for
speciﬁc jobs, no way to swap between them.

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Different Situations,
Different Behaviours

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• So we know that different situations are going to
call for different behaviours

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• We need to be able to swap from one to another
realistically

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• We need to be able to swap from one to another
realistically
• This is where we’re going to really be using AI
techniques

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Finite State Machines

• FSMs are fundamental building blocks in Computer
Science

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Science
• A machine that can be in one of a number of states

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Science
• A machine that can be in one of a number of states
• It transitions from one state to another when
certain conditions are met

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NPCs as an FSM

• We can represent what behaviour is currently active
as a state in an FSM

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NPCs as an FSM

• When certain things happen we can transition to a
different state

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NPCs as an FSM

• When certain things happen we can transition to a
different state
• We can move around states as required

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A Simple Guard

• Let’s combine what we’ve seen already

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A Simple Guard

‣ A guard stands idle

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A Simple Guard

‣ When he sees the player he begins approaching

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A Simple Guard

‣ When he sees the player he begins approaching
‣ If the player breaks line of sight, he returns to his start
position

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A Simple Guard

• Demo 4

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Non-Boolean Transitions

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• We can track non-boolean values and use these as
triggers for state transitions

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• We can track non-boolean values and use these as
triggers for state transitions
• Just need to meet a boolean criteria e.g. X > 100

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Suspicious Guard

• This time, the guard will grow suspicious the longer
the player stays in view.

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Suspicious Guard

‣ When suspicion hits a threshold, the guard will
investigate

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Suspicious Guard

investigate
‣ If the player breaks line of sight, guards suspicion begins
to lower, continues to investigate

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Suspicious Guard

investigate
‣ If the player breaks line of sight, guards suspicion begins
to lower, continues to investigate
‣ When suspicion drops below a threshold, returns to start

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Suspicious Guard

• Demo 5

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Beyond FSMs

• Finite State Machines are easy to understand

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Beyond FSMs

• But they get complicated quickly

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Beyond FSMs

‣ Particularly for behaviours that need to have multiple
components chained to them, and be interruptible

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Beyond FSMs

• They aren’t state of the art, but good to introduce
concepts

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Beyond FSMs

• They aren’t state of the art, but good to introduce
concepts
• Not widely used in industry now.
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Hierarchical Concurrent
State Machines

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State Machines

• Recent addition to the AI developers arsenal

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State Machines

• Popular after successful use in Left 4 Dead

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State Machines

‣ Drove NPCs

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State Machines

‣ Drove NPCs
‣ Also drove the AI Director system

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State Machines

‣ Drove NPCs
‣ Also drove the AI Director system
• A real-time parallel redesign of the Finite State
Machine (sort of)

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State Machines

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State Machines

• Building block of an HCSM is itself an HCSM

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State Machines

‣ Recursive by nature

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State Machines

• Analogue input wire

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State Machines

• Conﬁguration parameters

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State Machines

• Conﬁguration parameters
• Output wire

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State Machines

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Behaviour Trees

• Behaviour Trees are another great way of deciding
which of a bunch of behaviours should be activated
at a given time.

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Behaviour Trees

at a given time.
• Lets you craft intricate behaviours, and combine
them relatively intelligently.

38

Behaviour Trees

at a given time.
• Lets you craft intricate behaviours, and combine
them relatively intelligently.
• Kind of similar conceptually to a Decision Tree with
an internal knowledge of what triggered last time
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Example Behaviour Tree

“Introduction to Behaviour Trees”, Bjoern Knaﬂa
AltDevBlogADay.com
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Path Finding

• Being able to automatically work out how to move
around a world is a common AI problem

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Path Finding

• Several good algorithms for doing this, today going
to show A*

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Path Finding

to show A*
• Works by assessing

40

Path Finding

to show A*
‣ Work done

40

Path Finding

to show A*
‣ Work done
‣ Estimating work remaining (a heuristic)

40

Path Finding

to show A*
‣ Work done
‣ Estimating work remaining (a heuristic)
• Always selects most promising node
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Heuristic Example - A*

B

A

42


B

A

43


B

1+7 A

1 +7

44


B

1+7 A

1 +7

45


B

2+6

1+7 A

2 + 8 1 +7

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B

2+6

1+7 A

2 + 8 1 +7

47


B

3+5

2+6

1+7 A

2 + 8 1 +7

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B

3+5

2+6

1+7 A

2 + 8 1 +7

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4+4 B

3+5 4+4

2+6

1+7 A

2 + 8 1 +7

50


4+4 5+3 B

3+5 4+4 5+3

2+6

1+7 A

2 + 8 1 +7

51


4+4 5+3 6+2 B

3+5 4+4 5+3

2+6 6+4

1+7 A

2 + 8 1 +7

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4+4 5+3 6+2 B

3+5 4+4 5+3

2+6 6+4

1+7 A

2 + 8 1 +7

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4+4 5+3 6+2 B

3+5 4+4 5+3

2+6 6+4

1+7 A

2 + 8 1 +7 2 + 6

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4+4 5+3 6+2 B

3+5 4+4 5+3

2+6 6+4

1+7 A

2 + 8 1 +7 2 + 6

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4+4 5+3 6+2 B

3+5 4+4 5+3

2+6 6+4

1+7 A

2 + 8 1 +7 2 + 6 3 + 5

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4+4 5+3 6+2 B

3+5 4+4 5+3

2+6 6+4

1+7 A 4+4

2 + 8 1 +7 2 + 6 3 + 5 4 + 4

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4+4 5+3 6+2 B

3+5 4+4 5+3

2+6 6+4

1+7 A 4+4

2 + 8 1 +7 2 + 6 3 + 5 4 + 4

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4+4 5+3 6+2 B

3+5 4+4 5+3

2+6 6+4 5+3

1+7 A 4+4 5+3

2 + 8 1 +7 2 + 6 3 + 5 4 + 4

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4+4 5+3 6+2 B

3+5 4+4 5+3

2+6 6+4 5+3

1+7 A 4+4 5+3

2 + 8 1 +7 2 + 6 3 + 5 4 + 4

60


4+4 5+3 6+2 B

3+5 4+4 5+3

2+6 6+4 5+3 6+2

1+7 A 4+4 5+3

2 + 8 1 +7 2 + 6 3 + 5 4 + 4

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4+4 5+3 6+2 B

3+5 4+4 5+3

2+6 6+4 5+3 6+2

1+7 A 4+4 5+3

2 + 8 1 +7 2 + 6 3 + 5 4 + 4

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4+4 5+3 6+2 B

3+5 4+4 5+3 7+1

2+6 6+4 5+3 6+2

1+7 A 4+4 5+3

2 + 8 1 +7 2 + 6 3 + 5 4 + 4

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4+4 5+3 6+2 B

3+5 4+4 5+3 7+1

2+6 6+4 5+3 6+2

1+7 A 4+4 5+3

2 + 8 1 +7 2 + 6 3 + 5 4 + 4

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4+4 5+3 6+2 B

3+5 4+4 5+3 7+1

2+6 6+4 5+3 6+2

1+7 A 4+4 5+3

2 + 8 1 +7 2 + 6 3 + 5 4 + 4

65


4+4 5+3 6+2 B

3+5 4+4 5+3 7+1

2+6 6+4 5+3 6+2

1+7 A 4+4 5+3

2 + 8 1 +7 2 + 6 3 + 5 4 + 4

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Pathﬁnding in Unity

• Unity 3.5 now includes pathﬁnding natively

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‣ Pro only apparently

67


• Not entirely sure how this is implemented

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‣ I still haven’t bought Pro...

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‣ I still haven’t bought Pro...
• Possibly similar to the Path package (creator of that
works at Unity)

67

Path

• Demo 6

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Runtime Woes

• We’ve talked about pathﬁnding and decision making

69

Runtime Woes

• These can be computationally hard problems

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Runtime Woes

‣ In general we want to shoot for around 60fps

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Runtime Woes

‣ 16.67ms of computation per frame

69

Runtime Woes

‣ Most of that will go on non-AI aspects

69

Runtime Woes

• Realistically looking at 1 or 2ms per frame for all AI

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Runtime Woes

• Realistically looking at 1 or 2ms per frame for all AI
• Lots of computation, not a lot of time

69

Breaking Problems Up

• So we know that we might want to do things take
longer than we have.

70


• How can we break it up into manageable chunks so
that we aren’t delaying the game thread?

70


• In Unity, we have a very powerful system at our
disposal called

70


• In Unity, we have a very powerful system at our
disposal called
‣ Coroutines

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Coroutines

• Coroutines are blocks of code that can return a
value partway through execution.

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Coroutines

• Next time that routine is called, it will pick up
where it left off

71

Coroutines

where it left off
• Allows you to chop a block of code up into roughly
frame-computable chunks.

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Coroutines

where it left off
• Allows you to chop a block of code up into roughly
frame-computable chunks.
‣ Delays overall result, doesn’t delay code execution

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Juggling Timings

• Another great use of Coroutines is to get timing of
things right.

72

Juggling Timings

things right.
- Lots of things in Unity are “delayed calls”

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Juggling Timings

things right.

• They don’t actually happen til the end of the frame

72

Juggling Timings

things right.

- Destroy(), Debug.Log()

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Juggling Timings

things right.

• We can do things like

72

Juggling Timings

things right.

yield return new WaitForEndOfFrame();

72

Juggling Timings

things right.

yield return new WaitForEndOfFrame();
• What we are waiting for happens, code resumes
72

Coroutines in practice

• Demo

73

Summary

• Today we covered basics/fundamentals

74

Summary

‣ What we’re trying to achieve with Game AI

74

Summary

‣ Some very simple implementations of behaviours

74

Summary

‣ Simple methods for selecting which is active

74

Summary

‣ Discussion of some more advanced methods

74

Summary

‣ Intro to Pathﬁnding

74

Summary

‣ Intro to Pathﬁnding
‣ Coroutines for AI

74

Takeaways

1. AI is awesome

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Takeaways

1. AI is awesome
2. AI is complicated

75

Takeaways

1. AI is awesome
2. AI is complicated
3. AI doesn’t need to be scary

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But Wait There’s More!

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• We haven’t scratched the surface of what we can do
with AI.

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with AI.
‣ AI for companions

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with AI.
‣ AI for squad behaviours & coordination of multiple NPCs

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with AI.
‣ AI for scenario control (e.g. Left 4 Dead)

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with AI.
‣ AI for scenario control (e.g. Left 4 Dead)
‣ AI for content generation

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Contact

• luke@cis.strath.ac.uk
• @LukeD

• http://saig.cis.strath.ac.uk - Research Group
• http://lukedicken.com - Personal Site

Questions?
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Game AI 101 - NPCs and Agents and Algorithms... Oh My!

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