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An Introduction to Knowledge Representation. Beyond Behavior. Damián Isla, Naimad Games Peter Gorniak, Rockstar. Knowledge Representation. We spend a lot of time on what our AIs do but very little time on what they know One of the great neglected problems of [game] AI This talk:

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An introduction to knowledge representation l.jpg

An Introduction to Knowledge Representation

Beyond Behavior

Damián Isla, Naimad Games

Peter Gorniak, Rockstar


Knowledge representation l.jpg

Knowledge Representation

We spend a lot of time on what our AIs do

but very little time on what they know

One of the great neglected problems of [game] AI

This talk:

  • Introduce techniques

  • Agitate


Behavioral knowledge l.jpg

“Behavioral” Knowledge?

  • Behavioral knowledge

    • When to run away, when to shoot, when to flank left, etc.

    • Does an ant “know” where the anthill is?

  • State Knowledge

    • What is true about the world


The point of kr l.jpg

The Point of KR

Perception of a thing != the thing itself


The point of kr5 l.jpg

The Point of KR

Agent

Object 1

Pathfinding

Behavior

Object 2

Animation

Object 3


The point of kr6 l.jpg

The Point of KR

Agent

Object 1

KR

Pathfinding

Behavior

Object 2

Animation

Object 3

Decisions about

action

Decisions about

perception and interpretation


Why is kr interesting l.jpg

Why is KR Interesting?

  • Fun

    • exploit mistakes / limited perception

    • new modes of interaction

  • Lifelike

    • reason about AI as thinking perceiving creatures

    • emotional reactions

  • We’re doing it already anyway

  • Search for better representations == Search for more expressive power

    • build behavior out of better primitives


Timescales l.jpg

Timescales

“Guy X is behind the crate”

“I have three bullets left”

“That car is coming towards me”

“dogs are animals”

“birds have wings”

“pushing the button calls the elevator”

“Bobby is 5 years old”

“Jane is spending the semester in France.”

This instant


3 key concepts l.jpg

3 Key Concepts

  • Confidence

    • How sure am I in the knowledge I have?

  • Salience

    • How important is the sensory data I’m getting?

  • Prediction

    • What do believe will happen given what I’ve seen and what I know?


Slide10 l.jpg

Demo


Behavior update l.jpg

Behavior update

void s_agent::behavior_update()

{

if (!confused())

{

s_pos2d pos;

omap.get_target_position(&pos);

move_to(pos);

}

}


Behavior update12 l.jpg

Behavior update

void s_agent::behavior_update()

{

if (!confused())

{

s_pos2d pos;

omap.get_target_position(&pos);

move_to(pos);

}

}

+


Expectation related emotions l.jpg

Expectation-related Emotions

  • Confusion

  • Surprise

<Something I was confident in is confirmed FALSE>

<Something I thought unlikely is confirmed TRUE>


Expectation related emotions14 l.jpg

Expectation-related Emotions

  • Confusion

  • Surprise


Target lists l.jpg

Target Lists


Target lists16 l.jpg

Target Lists

Agent

Object 1

KR

Pathfinding

Behavior

Object 2

Animation

Object 3


Target lists17 l.jpg

Target Lists

Agent

Object 1

Target 1

Pathfinding

Behavior

Object 2

Target 2

Animation

Object 3

Target 3


Target lists18 l.jpg

Target Lists

Target

Perceived data

location(x,y,z)

actionshoot

hitpoints44

Derived data

Threat0.8

Target weight0.9

“Intentions”hurt_me

Allows AI to make mistakes

0.99

0.99

0.99

0.8

0.95

0.98

0.6

0.9

0.98

Shared computation

+

expressive power


Example l.jpg

shoot_at_target

shoot_at_target

Example

switch_to_knife

switch_to_knife

search_for_target

search_for_target

!


Slide20 l.jpg

Phil Carlisle Memorial

Memory Slide


Pcmms l.jpg

PCMMS

Volatile

behavior

state

  • Working memory

  • Short-term

  • Episodic

    • ???

Remember that

Target

Perceived data

location(x,y,z)

Target

Perceived data

location


Challenge 1 representational versatility l.jpg

Challenge #1:Representational Versatility

Solution: Polymorphism

Nazi

Truck

Wheel

Fence

Grass

My hand


Polymorphism l.jpg

Polymorphism

Percept DAG

(Synthetic Characters, MIT Media Lab, circa 2002)


Challenge 2 performance l.jpg

Challenge #2: Performance


Challenge 2 performance25 l.jpg

Challenge #2: Performance

Object 1

KR

Agent

Agent

Object 2

Agent

Object 3

Shared KR


Challenge 2 performance26 l.jpg

Challenge #2: Performance

X:location:

<x,y,x>

crates

Object 1

Agent

KR

KR

Agent

Object 2

KR

Agent

Object 3

X:weapon:

“pistol”

KR

enemies

Hybrid KR


Challenge 2 performance27 l.jpg

Challenge #2: Performance

Enemy 1

Agent

Enemy 2

O

A

Agent

Grass

Salience Threshold

O x A


Benefits of target lists l.jpg

Benefits of Target Lists

  • Reasonable mistakes / limited perception

  • Shared computation

  • Expressive power


Limitations of target lists l.jpg

Limitations of Target Lists

  • Relational information

    • Where does the notion of “behind” live?

  • Wholes and parts

    • Does a car’s wheel deserve it’s own representation?

    • A guy’s arm?

    • What about a mob of guys?


Slide30 l.jpg

Head

Enemy

has-a

next-to

Arm

Enemy

next-to

Arm

Representational Wankery

behind

holding

Car

has-a

Hood

Gun

Wheel

Wheel

Wheel

Wheel


Wild speculation l.jpg

Wild Speculation

Lazy Representation???

  • Perception is active

  • Behavioral / emotional / motivational state changes the way you see the world

  • And WHAT you see in the world.


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