Q&A Session with Game Developer

pucefakeAI and Robotics

Nov 30, 2013 (3 years and 10 months ago)

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Q&A Session with Game Developer


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Administrative: Game Design Analysis


Deadline
written document:
November 18
th
in class
,
PRINTED!



Deadline PowerPoint presentation:
November 17
th

until
6AM
(EST time) has to be e
-
mailed to
instructor



If
you haven't done so, start working on this now!


Don’t improvise, don’t wait until the last minute




Administrative


Test # 2: Friday November 8
th
.


It covers:

1.
All of Unit 2 in the book

2.
All topics we covered in the lectures after Test #
1


Similar in style to Test #1


Meaning you really have to study (1) and (2) above


Idea: work on Game Design Analysis o help
prepare for the test


Don’t improvise/wait for the last minute to
study


Games as Information Systems

(Ch. 17)


information
” in Information Systems


From the perspective of
Information Theory

(Ch. 16),
information is a non
-
semiotic artifact


In contrast, for
Information Systems
, “
information”
has
meaning. Includes everything from data to knowledge


Under this view games put information at play


Classical Example:

The constitutive rules of
poker can be viewed as
a game where inference
is made from imperfect
information

Another
example


Kinds of Information in a Game


Information known to all players




Information known to only one
player




Information known to the game
only




Randomly generated information

(
clip from Civilization
IV)


Economy of Information


Crucial game design question: how much information
you
are going
to show to the
player?


Hiding information is a good way to caught players
interest.
Example of hidden information that is
revealed while playing:











State information in
imperfect information
games.


Fog of war


Plot


Adventure games


Player skills


RPG games


Rules of the game


Learning through play

Games as Cybernetic Systems
(Ch. 18)

Cybernetics



Resulted from Information Theory (Ch.
16) and Information Systems Theory (Ch.
17
)


Focus on how dynamic systems change
over
time


Cybernetics is used to study organizations


Large companies


Governments


Cybernetics is also used in Operations
Research and Machine Learning




Basic principle: output
-
feedback
-
adjustment

Elements of a Cybernetic System

“The feedback Loop”

Environment

Comparator

Sensor

Activator



AC
-
unit
-
in
-
a
-
room
example



Heater
-
unit
-
in
-
a
-
room example

feedback

adjustment

output

Kinds of Feedback



Example of each for the AC
-
unit
-
in
-
a
-
room example



Negative
: temperature(room) > 75


then activate
cooler



Positive
: temperature(room)
>
75


then activate
heater

Simple Cybernetic Design


Lets combine two feedback loops that maintains the
temperature in a room stays between 65


and 75



We have
an AC unit and


We have a
heater



Lets do one
that
maintains the temperature in a room at
70

. Same conditions as before

Example of “this stuff” in games?


Positive/negative feedback in games
?



An example of
positive
feedback



An example of
negative
feedback

Feedback
Loops
in Games

(
Marc LeBlanc)

Environment

Comparator

Sensor

Activator

feedback

adjustment

output

Game state

Game mechanical bias

Scoring function

Game Controller

Game state


Information known to all players


Information known to only one player


Information known to the game only


Randomly generated information

Example of negative Feedback:
Downforce

http://www.youtube.com/watch?v=37g5uNwmqz4


http://www.youtube.com/watch?v=z
-
OQzqUdbs4



Negative:


Simulated
momentum vs
.
player


AI lets itself
catch
-
up if you
are loosing


AI catches
up if you are
winning



AI lets itself catch
-
up if you are loosing

feedback

adjustment

output

Game state

Game mechanical bias

Scoring function

Game Controller


Position of autos


Configuration of
track





Player loosing?
Formally:

Distance(player, finish)
> Distance(leadingCar,
finish)


Player position, leadingCar
position


Formally:
Distance(player,finish),
Distance(leadingCar,finish)


Slow down leading
-
car



Formally:

speed(leadingCar)



speed(player)



f(
Distance(player, leadingCar)

Simulated gravity vs player control

feedback

adjustment

output

Game state

Game mechanical bias

Scoring function

Controller


Position of autos


Configuration of
track


speed…


Player going out of
road?



Player direction


Road direction


Steer car towards road


Mortal Combat: combo

feedback

adjustment

output

Game state

Game mechanical bias

Scoring function

Controller


Health Points
player


Health points
opponent


Disabled (Yes,
No)


Opponent
situation (chance
for next combo,
no chance)



Disabled = Yes


Opponent situation =
chance for next combo


Disabled,


Opponent situation


Disabling attack


Using
F
eedback Loops:
Difficulty Levels


Brigette

Swan


Adaptation to the quirks and
habits of a particular player over
time: reinforcement learning


Many games implement
difficulty sliders.


Common:


start early levels easy


More difficult as game
progresses


Difficulty can
be
amount of
information available!


Dynamic Difficulty
Adjustment (DDA)

Dynamic Difficulty Adjustment (DDA)

--

The Oblivion Controversy


Idea: adjust game so that it remains challenging
(negative feedback)



It is an RPG game like say Diablo but…



As your avatar levels so do all mobs in the game


So for example you “clean” a dungeon at
level 1 killing some rats, at level 10 those rats
will be armored and will hit much harder



Does it still have meaningful play as a result?


Use of Feedback in Games


(
Marc LeBlanc)


Stability
:


Negative feedback stabilizes a game


Positive feedback destabilizes a game


Game duration


Negative feedback can prolong a game


Positive feedback can end it


Success
:


Positive feedback magnifies early success


Negative feedback magnifies late ones


Control
:


Feedback systems can emerge from games


Feedback systems can take control away from gamers


… and result in lost of meaningful play!

Examples?

Announcement: Talk Tomorrow

"Building a science of narrative: Computational
contributions to the study of stories and their telling"


R. Michael Young

Professor, Department of Computer Science

North Carolina State University


Tuesday, October 29, 4:00 PM

Lewis Lab Room 316