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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
An Introduction to Knowledge Representation

Beyond Behavior

Damián Isla, Naimad Games

Peter Gorniak, Rockstar

knowledge representation
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
“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
The Point of KR

Perception of a thing != the thing itself

the point of kr5
The Point of KR

Agent

Object 1

Pathfinding

Behavior

Object 2

Animation

Object 3

the point of kr6
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
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
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
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?
behavior update
Behavior update

void s_agent::behavior_update()

{

if (!confused())

{

s_pos2d pos;

omap.get_target_position(&pos);

move_to(pos);

}

}

behavior update12
Behavior update

void s_agent::behavior_update()

{

if (!confused())

{

s_pos2d pos;

omap.get_target_position(&pos);

move_to(pos);

}

}

+

expectation related emotions
Expectation-related Emotions
  • Confusion
  • Surprise

<Something I was confident in is confirmed FALSE>

<Something I thought unlikely is confirmed TRUE>

target lists16
Target Lists

Agent

Object 1

KR

Pathfinding

Behavior

Object 2

Animation

Object 3

target lists17
Target Lists

Agent

Object 1

Target 1

Pathfinding

Behavior

Object 2

Target 2

Animation

Object 3

Target 3

target lists18
Target Lists

Target

Perceived data

location (x,y,z)

action shoot

hitpoints 44

Derived data

Threat 0.8

Target weight 0.9

“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

shoot_at_target

shoot_at_target

Example

switch_to_knife

switch_to_knife

search_for_target

search_for_target

!

pcmms
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
Challenge #1:Representational Versatility

Solution: Polymorphism

Nazi

Truck

Wheel

Fence

Grass

My hand

polymorphism
Polymorphism

Percept DAG

(Synthetic Characters, MIT Media Lab, circa 2002)

challenge 2 performance25
Challenge #2: Performance

Object 1

KR

Agent

Agent

Object 2

Agent

Object 3

Shared KR

challenge 2 performance26
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
Challenge #2: Performance

Enemy 1

Agent

Enemy 2

O

A

Agent

Grass

Salience Threshold

O x A

benefits of target lists
Benefits of Target Lists
  • Reasonable mistakes / limited perception
  • Shared computation
  • Expressive power
limitations of target lists
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

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
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.