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

2

Who Am I

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

2

Who Am I

• Founding member of SAIG

2

Who Am I

• AltDevBlogADay author

2

Who Am I

• AltDevConf organiser

2

Who Am I

• Board of Directors for IGDA Scotland

2

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

3

Game AI 101

• We will be dealing with a mix of

3

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

4

Pure AI vs Game AI

‣ Create “realistic simulations” of intelligence

4

Pure AI vs Game AI

- E.g. Believable characters

4

Pure AI vs Game AI

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

4

Pure AI vs Game AI

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

6

Pure AI vs Game AI

• Is that what we actually want to achieve?

6

Pure AI vs Game AI

• Game AI aims to make an immersive experience

6

Pure AI vs Game AI

‣ Represent a thug character accurately

6

Pure AI vs Game AI

‣ Allow the player to “become” Batman

6

Pure AI vs Game AI

‣ Allow the player to “become” Batman
• Entertainment, not problem solving
6

Automated Opponents

• Centuries of trying to play games with few players

7

Automated Opponents

‣ Mechanical Turk claimed to be a chess player circa 1770

7

Automated Opponents

‣ First video game example - Pong 1972

7

Automated Opponents

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

7

Automated Opponents

• Emphasis now on delivering a good player
experience.

7

Automated Opponents

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

7

Automated Opponents

experience.
‣ Human-like rather than “good” AI
‣ Sub-human in order to be beatable
7

Looking and Being Smart

• Two components to Game AI

8


‣ Looking Smart

8


‣ Looking Smart
‣ Being Smart

8


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

8


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

8


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

8


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

8


‣ Looking Smart
‣ Being Smart
• Good decision making still required to drive this
‣ Be smart, then trigger equivalent “look smart”
8

What is
Artiﬁcial Intelligence?

10

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

10

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

10

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

10

What is
choices.
‣ Tho neither do people...
• Typically, looking for better-than-random levels of
sophistication
10

Decision Making

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

11

Decision Making

• Science of decisions can ﬁll multiple degree-level
courses

11

Decision Making

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

11

Decision Making

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

11

Smart or Dumb?

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

12

Smart or Dumb?

‣ Making a decision is a good start

12

Smart or Dumb?

• Partly because of the entertainment issue

12

Smart or Dumb?

‣ Making good decisions is irrelevant

12

Smart or Dumb?

‣ We don’t want tactical genius soldiers

12

Smart or Dumb?

• Partly for rapid iteration

12

Smart or Dumb?

• Partly for rapid iteration
‣ Make any decision, ﬁgure out how to make it better later
12

Agent-Based Systems

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

13

Agent-Based Systems

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

13

Agent-Based Systems

• Agents have three important characteristics

13

Agent-Based Systems

‣ Sense the environment

13

Agent-Based Systems

‣ “Think” about what to do

13

Agent-Based Systems

‣ “Think” about what to do
‣ Turn “thinking” into actions within the environment
13

NPCs as Agents

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

14

NPCs as Agents

• Each NPC is an agent.

14

NPCs as Agents

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

14

NPCs as Agents

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

14

NPCs as Agents


• In this model

14

NPCs as Agents


• In this model
‣ NPCs sense the world

14

NPCs as Agents


• In this model
‣ NPCs sense the world
‣ Decide what to do, then do it
14

Representations

• Software Engineering buzzwords!

15

Representations

• Representation internal to one NPC or shared?

15

Representations

‣ Alien Hivemind - All units know what one unit knows

15

Representations

- Communal blackboard!

15

Representations

‣ Modern Squad - Communication between certain units

15

Representations

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

15

Representations

- Message passing between units

15

Representations

‣ Medieval Guards - No widespread communication

15

Representations

‣ Medieval Guards - No widespread communication
- Pure-internal representation
15

Taking Our First Steps

• For this session let’s assume we have

17


‣ A 3D world

17


‣ A 3D world
‣ A pre-fab for an NPC

17


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

17


‣ A 3D world

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

17


‣ 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 )
17

Moving at Random

• OK, let’s do something basic

18

Moving at Random

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

18

Moving at Random

‣ When he gets there, pick another location etc.

18

Moving at Random

• We need to

18

Moving at Random

• We need to
‣ Pick a location randomly

18

Moving at Random

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

18

Moving at Random

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

18

Moving at Random

• Demo 1

19

Following the Player

• Let’s do something a bit more sophisticated

20


‣ Follow the player around the map

20


• This time we need to

20


‣ Determine position of the target

20


‣ Move towards the target

20


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

20


• Demo 2

21

On Patrol

• Typically, we want soldiers guarding things, patrolling

22

On Patrol

‣ Go to the next waypoint

22

On Patrol

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

22

On Patrol

• Still just an iterative improvement on previous

22

On Patrol

‣ We need to track where the waypoints are

22

On Patrol

‣ Which one we are near

22

On Patrol

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

22

On Patrol

• Demo 3

23

Looking (Kinda) Smart

• An NPC that does just one thing is not smart

24


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

24


• 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.

24

Different Situations,
Different Behaviours

25


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

25


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

25


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

25

Finite State Machines

• FSMs are fundamental building blocks in Computer
Science

26


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

26


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

26

NPCs as an FSM

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

28

NPCs as an FSM

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

28

NPCs as an FSM

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

28

A Simple Guard

• Let’s combine what we’ve seen already

29

A Simple Guard

‣ A guard stands idle

29

A Simple Guard

‣ When he sees the player he begins approaching

29

A Simple Guard

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

29

A Simple Guard

• Demo 4

30

Non-Boolean Transitions

31


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

31


• 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

31

Suspicious Guard

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

32

Suspicious Guard

‣ When suspicion hits a threshold, the guard will
investigate

32

Suspicious Guard

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

32

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

32

Suspicious Guard

• Demo 5

33

Beyond FSMs

• Finite State Machines are easy to understand

34

Beyond FSMs

• But they get complicated quickly

34

Beyond FSMs

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

34

Beyond FSMs

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

34

Beyond FSMs

• They aren’t state of the art, but good to introduce
concepts
• Not widely used in industry now.
34

Hierarchical Concurrent
State Machines

35

State Machines

• Recent addition to the AI developers arsenal

35

State Machines

• Popular after successful use in Left 4 Dead

35

State Machines

‣ Drove NPCs

35

State Machines

‣ Drove NPCs
‣ Also drove the AI Director system

35

State Machines

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

35

State Machines

36

State Machines

• Building block of an HCSM is itself an HCSM

36

State Machines

‣ Recursive by nature

36

State Machines

• Analogue input wire

36

State Machines

• Conﬁguration parameters

36

State Machines

• Conﬁguration parameters
• Output wire

36

State Machines

37

Behaviour Trees

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

38

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
38

Example Behaviour Tree

“Introduction to Behaviour Trees”, Bjoern Knaﬂa
AltDevBlogADay.com
39

Path Finding

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

40

Path Finding

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

40

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
40

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

46


B

2+6

1+7 A

2 + 8 1 +7

47


B

3+5

2+6

1+7 A

2 + 8 1 +7

48


B

3+5

2+6

1+7 A

2 + 8 1 +7

49


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

52


4+4 5+3 6+2 B

3+5 4+4 5+3

2+6 6+4

1+7 A

2 + 8 1 +7

53


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

54


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

55


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

56


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

57


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

58


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

59


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

61


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

62


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

63


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

64


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

66

Pathﬁnding in Unity

• Unity 3.5 now includes pathﬁnding natively

67


‣ Pro only apparently

67


• Not entirely sure how this is implemented

67


‣ I still haven’t bought Pro...

67


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

67

Path

• Demo 6

68

Runtime Woes

• We’ve talked about pathﬁnding and decision making

69

Runtime Woes

• These can be computationally hard problems

69

Runtime Woes

‣ In general we want to shoot for around 60fps

69

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

69

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

70

Coroutines

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

71

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.

71

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

71

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”

72

Juggling Timings

things right.

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

72

Juggling Timings

things right.

- Destroy(), Debug.Log()

72

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

75

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

75

But Wait There’s More!

76


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

76


with AI.
‣ AI for companions

76


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

76


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

76


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

76

Contact

• luke@cis.strath.ac.uk
• @LukeD

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

Questions?
77

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

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

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

Similaire à Game AI 101 - NPCs and Agents and Algorithms... Oh My! (20)

Plus de Luke Dicken

Plus de Luke Dicken (20)

Dernier

Dernier (20)

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

Notes de l'éditeur