This talk was given at the symposium on procedural content generation at ITU Copenhagen, November 2014. It outlines the major motivations for doing research in PCG, identifies historical trends, and asks questions about where we are going next.

1. understanding
our
past
to
improve
our
future
Gillian
Smith

2. PCG
TRIVIA
GAME
let’s
play!

3. What
games
are
these
quotes
describing?
"Exploration
is
seeing
things
that
no-­‐one
else
has
ever
seen
before.
Every
creature
...
is
unique.”
No
Man's
Sky,
2015
"Computerized
solitaire
dungeons,
each
one
will
be
unique
(never
two
exactly
alike).”
A
Computer
Generated
Dungeon,
1977
"randomized
levels,
the
relentless
onslaught
of
monsters
and
events
in
a
perpetually
fresh
world”
Diablo
III,
2012
"throw
a
monster
[at
players]
that
keeps
them
on
their
toes,
one
that
they
have
never
seen
before
because
it
is
unique."
Random
Monsters
D&D
Supplement,
1980

4. What
games
are
these
quotes
describing?
"Exploration
is
seeing
things
that
no-­‐one
else
has
ever
seen
before.
Every
creature
...
is
unique.”
No
Man's
Sky,
2015
"Computerized
solitaire
dungeons,
each
one
will
be
unique
(never
two
exactly
alike).”
A
Computer
Generated
Dungeon,
1977
"randomized
levels,
the
relentless
onslaught
of
monsters
and
events
in
a
perpetually
fresh
world”
Diablo
III,
2012
"throw
a
monster
[at
players]
that
keeps
them
on
their
toes,
one
that
they
have
never
seen
before
because
it
is
unique."
Random
Monsters
D&D
Supplement,
1980

5. Motivations
for
Generating
Content
§ Data
compression
§ Replayability
§ Personalization
Platformer
AI
Competition,
2013
Elite,
1984
Diablo
III,
2012

6. Motivations
for
Generating
Content
§ Data
compression
§ Replayability
§ Personalization
Platformer
AI
Competition,
2013
Elite,
1984
Diablo
III,
2012

7. We
know
how
to
do
this!

8. But…
design
tools?
"Build
the
whole
adventure
yourself
or
ask
the
computer
to
do
it
for
you."
"Begin
the
construction
process
with
as
much
or
little
as
you
like...”
Adventure
Construction
Set,
1984
"[it
creates]
a
different
maze
every
time
it
is
run
and
guarantee
only
one
path
through”
AMAZIN,
1973
"[generated
monsters]
created
by
computer
program
to
completely
eliminate
bias
and
error”
Trolls
&
Trollkin,
1979

9. Getting
stuck
in
a
motivation
rut?

10. Our
Motivations
and
Backgrounds
§ AI:
testing
AI
through
games
as
a
testbed
§ HCI:
helping
human
designers
§ Creativity:
modeling
(human?)
creativity
§ Game
industry:
solving
authoring
burden
§ Game
design:
designing
innovative
and
fun
games
§ Making:
the
joy
of
making
things
that
make
things

11. HISTORICAL
TRAJECTORIES
where
have
we
been
and
where
can
we
go?

12. Data
to
Process
Intensive
data-­‐intensive
Robot
Unicorn
Attack
process-­‐intensive
Petalz

13. Deeply
Process
Intensive
PCG

14. Deeply
Process
Intensive
PCG
§ Drawing
inspiration
from
other
sources
§ Rationalizing
decision-­‐making

15. Static
to
Playable
Content
static
content
Speed
Tree
playable
content
Angelina

16. Deeply
Playable
Content

17. Deeply
Playable
Content
§ New,
themed
game
mechanics
§ New
game
genres

18. Developer
to
Player
Control
developer
control
Elite
player
control
Spore

19. Players
and
Beyond

20. Players
and
Beyond
§ “Magic
crayons”
for
playable
content
§ Beyond
players:
communities,
observers

21. Single
to
Multiple
Players
single
player
Rogue
multiple
players
Galactic
Arms
Race

22. Multi-­‐Player,
Multi-­‐Creator

23. Multi-­‐Player,
Multi-­‐Creator
§ Content
for
many
players
across
instances
§ Collaborative
play

24. Common
Aesthetics
§ Discovery
§ Challenge

25. Common
Aesthetics
§ Discovery
§ Challenge
§ Fellowship
§ Sensation
§ Fantasy
§ Narrative
§ Expression
§ Submission

26. Summary
§ Process-­‐intensive
§ DePining
new
genres
§ Communities
of
players
§ Collaborative
play
§ Alternate
aesthetics

27. FUTURE
OF
PCG
DEVELOPMENT
best
practices
for
pcg
through
understanding
generator
history

28. Launchpad’s
Expressivity
1
level
>75
levels

29. Linearity

30. Linearity

31. Leniency

32. Leniency

33. Comparing
Generators

34. Open
Questions
in
Expressivity
Analysis
§ What
metrics
should
we
use?
§ Individual
levels
§ Pairwise
comparison
§ How
do
we
know
metrics
are
valid?
§ How
do
we
visualize
results?
§ How
does
this
apply
to
user-­‐created
content?

35. Expressive
Range
as
a
Debugging
Tool?
§ Static
analysis
lets
us
see
results
of
code
changes
quickly
§ Gain
better
understanding
of
generator
capabilities

36. Uncovering
Biases
all
lengths
length
5
length
10
length
15
length
20
all
types
regular
random
swing

37. FUTURE
OF
PCG-­‐BASED
GAMES
player
exploration
of
historical
choices

38. Co-­‐Design
of
PCG
and
Game

39. Launchpad
§ Rhythm-­‐based
§ Control
type
Parameterized
§ Control
extent
Compositional
Experiential

40. Endless
Web
Player
explores
the
Dream
to
Pind
and
rescue
six
dreamers
trapped
in
their
nightmares.

41. Themes:
Exploration
and
Creation
§ Players
navigate
physical
space
while
exploring
generative
space
§ Choices
lead
the
player
to
newly
generated
terrain
with
different
generation
parameters

42. Guiding
the
Player
Goal-­‐driven
exploration
of
generative
space

43. An
Early
Prototype…

44. “Mechanical
Experience”
§ Interaction
with
generator
§ Multiple
simultaneous
universes
unlocked
by
choice
§ Comparing
player
histories
§ Comparing
“parentage”
of
evolved
content

45. FUTURE
OF
PCG
DESIGN
TOOLS
a
generator
that
can
learn
from
its
global
history

46. Current
PCG
Design
Tools
§ Single
user
§ Single
AI
§ Static
heuristics
§ Unchanging
design
knowledge

47. Collaborative
Design
§ Conversation
between
active
participants
§ Participants
have
different
goals
and
expertise
§ Diversity
of
experiences
§ Prototype
and
discuss
different
ideas

48. Overriding
Human
Decisions
§ …but
for
a
good,
and
well-­‐explained,
reason!
§ Early
decisions
can
become
less
important
§ (Novice)
designers
struggle
with
undoing
work
they
have
already
done
§ Example:
removing
a
previously
agreed-­‐
upon
game
mechanic
due
to
a
change
in
the
story

49. Moving
Away
from
Turn-­‐Taking
§ Computer
can
work
independently
§ Computer
is
no
longer
purely
reactive,
must
be
proactive
in
conversation
§ Both
parties
may
interrupt
each
other
or
pause
to
think
§ Example:
interrupt
a
designer
with
a
new
idea
for
a
level
during
brainstorming

50. Negotiating
High-­‐Level
Design
Goals
§ Awareness
of
additional
constraints
§ Present
different
“opinions”
on
design
§ High-­‐level
understanding
leads
to
better
communication
§ Example:
properties
of
a
game
level
based
on
current
level
progression

51. Many
Research
Angles
§ Support
for
multiple
designers
§ Learning
from
its
own
history
§ Building
up
a
new
pattern
library
§ Integrating
playtesting
§ Understand
playtest
data
and
improve
§ Building
stronger
processes
§ Rationalizing
decisions
to
user
§ Overriding
human
decisions
§ Building
smarter
interfaces
§ Representing
AI
“thought”
to
designer

52. FUTURE
OF
PCG?
learning
from
our
past

53. Future
Directions
§ Finding
new
motivations
§ Extending
along
multiple
dimensions
§ Experimenting
with
new
aesthetics
§ Improving
development
practices
§ Enabling
new
experiences
§ Building
smarter
tools

54. Questions?
Thanks!
gillian@ccs.neu.edu
@gillianmsmith
http://www.sokath.com
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