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:
 Introduce techniques
 Agitate
“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
Perception of a thing
!= the thing itself
The Point of KR
Agent
Object 1
Pathfinding
Object 2
Behavior
Animation
Object 3
The Point of KR
Agent
Object 1
Pathfinding
Object 2
KR
Behavior
Animation
Object 3
Decisions about
perception and interpretation
Decisions about
action
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
“dogs are animals”
“birds have wings”
“pushing the button
calls the elevator”
∞
“Guy X is behind the crate”
“I have three bullets left”
“That car is coming
towards me”
“Bobby is 5 years old”
“Jane is spending the
semester in France.”
This
instant
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?
DEMO
Behavior update
void s_agent::behavior_update()
{
if (!confused())
{
s_pos2d pos;
omap.get_target_position(&pos);
move_to(pos);
}
}
Behavior update
void s_agent::behavior_update()
{
if (!confused())
{
s_pos2d pos;
omap.get_target_position(&pos);
move_to(pos);
}
}
+
Expectation-related Emotions
 Confusion
<Something I was confident in is confirmed FALSE>
 Surprise
<Something I thought unlikely is confirmed TRUE>
Expectation-related Emotions
 Confusion
 Surprise
TARGET LISTS
Target Lists
Agent
Object 1
Pathfinding
Object 2
KR
Behavior
Animation
Object 3
Target Lists
Object 1
Agent
Target 1
Pathfinding
Object 2
Target 2
Behavior
Animation
Object 3
Target 3
Target Lists
Target
Perceived data
location
action
hitpoints
Shared
computation
+
expressive
power
(x,y,z)
shoot
44
Derived data
Threat
0.8
Target weight 0.9
“Intentions” hurt_me
0.6
0.99
0.8
0.99
0.95
0.9
0.99
0.98
Allows AI
to make
mistakes
Example
shoot_at_target
switch_to_knife
search_for_target
Phil Carlisle Memorial
Memory Slide
PCMMS
Volatile
behavior
state
 Working memory
Remember that
Target
 Short-term
 Episodic
 ???
Perceived data
location
(x,y,z)
Challenge #1:
Representational Versatility
Truck
Wheel
Nazi Fence
Grass
My hand
Solution: Polymorphism
Polymorphism
Percept DAG
(Synthetic Characters, MIT Media Lab, circa 2002)
Challenge #2: Performance
Challenge #2: Performance
Object 1
Object 2
Object 3
Shared KR
Agent
KR
Agent
Agent
Challenge #2: Performance
Object 1
crates
X:location:
<x,y,x>
Agent
KR
Object 2
KR
Agent
KR
Object 3
Agent
X:weapon:
“pistol”
KR
enemies
Hybrid KR
Challenge #2: Performance
Enemy 1
Agent
Enemy 2
O
A
Agent
Grass
Salience Threshold
OxA
Benefits of Target Lists
 Reasonable mistakes / limited perception
 Shared computation
 Expressive power
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?
Head
Enemy
Arm
Enemy
Arm
Car
Hood
Gun
Wheel
Wheel
Wheel
Wheel
Wild Speculation
Lazy Representation???
1. Perception is active
2. Behavioral / emotional / motivational state
changes the way you see the world
3. And WHAT you see in the world.