Artificial Intelligence Based
Automatic Generation of
Entertaining Gaming Engines
Dr. Zahid Halim
Faculty of Computer Science and Engineering
Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi
zahid.halim@giki.edu.pk
19th June 2012
Layout
Artificial Intelligence
AI
What is not AI and application of AI
~ AI
Case Study
Results
Q/A
2
Nuts and bolts of a predator/prey gaming
engine
Results of the experiment
Questions
Artificial Intelligence based Automatic generation of Entertaining Gaming Engines
Artificial Intelligence (AI)
•
•
Intelligence is the computational part of the ability to
achieve goals in the world
One of the most dumbest thing in world is computer
•
Recall the two numbers addition program using int data
type
•
Artificial intelligence allows computers to
•
Think like humans
•
Learn from experience
•
Recognize patterns in large amounts of complex data
•
Make complex decisions based on knowledge and
reasoning skills
3
Artificial Intelligence based Automatic generation of Entertaining Gaming Engines
Everything
fast is not
AI
AI is not
NOT AI
•
•
•
A meter reading algorithm at
petrol pumps
Encyclopedia
SQL query
AI
•
•
•
4
TOPIO, humanoid robot can
play ping-pong with human
Speech and Voice
Recognition
Face recognition
Artificial Intelligence based Automatic generation of Entertaining Gaming Engines
AI tools
•
•
•
•
Artificial Neural Networks
Swarm Intelligence
Evolutionary Computation
Pruning Algorithms
:
:
:
(and the list goes on)
5
Artificial Intelligence based Automatic generation of Entertaining Gaming Engines
Automated Game
Generation
A Case Study
Predator/prey Games
Search Space
•
•
14 X 14 grid excluding the boundary
•
walls.
•
No movement
Couple of walls at fixed positions
•
Clockwise
•
Counter clockwise
•
Random
•
Random direction
and of size 7 cells
•
There is one player controlled by
the human player.
•
Movement logic
There are N (0-20)other pieces of M
•
Collision logic
(1,2 and 3) types
•
•
•
no effect
•
random relocation to a new
Maximum duration 100 game steps
Finish game
•
Agent dies
•
Maximum score is achieved
•
Maximum game steps
location on the grid
•
•
death.
Scoring logic
•
+1, -1, 0
utilized
7
Artificial Intelligence based Automatic generation of Entertaining Gaming Engines
Chromosome Encoding for Genetic
Algorithm
Number of
predators
Movement logic
Collision logic
8
Red
0-20
Blue-Green
0-2
Green
0-20
Blue-Blue
0-2
Blue
0-20
Blue-Agent
0-2
Red
0-4
Agent-Red
0-2
Green
0-4
Agent-Green
0-2
Blue
0-4
Agent-Blue
0-2
Red- Red
0-2
Red- Red
-1,0,+1
Red- Green
0-2
Green-Green
-1,0,+1
Red-Blue
0-2
Blue-Blue
-1,0,+1
Red- Agent
0-2
Agent-Red
-1,0,+1
Green-Red
0-2
Agent Green
-1,0,+1
Green-Green
0-2
Agent-Blue
-1,0,+1
Green-Blue
0-2
Green-Red
-1,0,+1
Green-Agent
0-2
Blue-Red
-1,0,+1
Blue-Red
0-2
Blue-Green
-1,0,+1
Collision
logic
Score logic
Artificial Intelligence based Automatic generation of Entertaining Gaming Engines
Entertainment Metrics
•
•
•
•
9
Duration of the Game
Appropriate Level of Challenge
Diversity
Usability
D = ( nK 0 Lk )/n
(
C=e
|S m  S a |
)
Sm
n
m
i 1
k 0
Div = ( ( (d k )))/n
n
m
i 1
k 0
U = ( ((  (C k )) / | Cu |))/n
Artificial Intelligence based Automatic generation of Entertaining Gaming Engines
Rule Based Controller
•
•
The controller looks up, down, left and right. It notes the nearest piece (if
any) in each of the four directions, and then it simply moves one step
towards the nearest score increasing piece
If there are no score increasing piece present it determines its step
according to the following priority list
• Move in the direction which is completely empty
• If more than one directions are empty move towards the farthest wall
• Move in the direction which contains a score neutral piece
• Move in the direction which contains a score decreasing piece
• Move in the direction which contains a death causing piece
10
Artificial Intelligence based Automatic generation of Entertaining Gaming Engines
Neural Network Based Controller
•
•
•
•
•
•
Multi-layer fully feed forward
6 neurons in the input layer
5 neurons in the hidden layer
4 output layer neurons
Sigmoid activation function
∆xr
Edges weights -5 to +5.
∆yr
∆xg
∆yg
∆xb
∆yb
11
C
o
n
n
e
c
ti
o
n
E
d
g
e
s
C
o
n
n
e
c
ti
o
n
E
d
g
e
s
C
o
n
n
e
c
ti
o
n
E
d
g
e
s
Nu
Nd
Nl
Nr
Artificial Intelligence based Automatic generation of Entertaining Gaming Engines
Experimentation Setup
•
10 chromosomes are randomly initialized by the GA
•
One offspring is created for each chromosome
•
•
•
•
Duplicating it
Mutating any one of its gene
Results in 20 chromosomes from which 10 best
chosen
100 generations
12
Artificial Intelligence based Automatic generation of Entertaining Gaming Engines
Duration of game
Predators
Movement logic
G
B
R
G
B
17
13
3
0
3
Collision logic
B -G
B-B
B-A
A-R
A-G
A-B
2
2
2
0
0
2
R
3
Predators
Movement logic
G
B
R
G
B
18
10
0
1
3
Collision logic
B -G
B-B
B-A
A-R
A-G
A-B
1
1
1
1
0
2
R
0
R-R
2
R-G
1
R-B
1
R-R
-1
G-G
-1
B-B
0
R-R
2
R-G
2
R-B
1
R-R
0
G-G
1
B-B
0
Collision logic
R-A
G-R
G-G
0
0
1
Scoring logic
A-R
A-G
A-B
-1
0
0
Collision logic
R-A
G-R
G-G
1
2
0
Scoring logic
A-R
A-G
A-B
1
-1
-1
G-B
0
G-A
2
B-R
1
G-R
-1
B-R
-1
B-G
0
G-B
0
G-A
1
B-R
1
G-R
1
B-R
-1
B-G
-1
(a)
(b)
Appropriate level of challenge
Predators
Movement logic
G
B
R
G
B
0
11
0
0
3
Collision logic
B -G
B-B
B-A
A-R
A-G
A-B
2
2
2
2
0
2
R
10
Predators
Movement logic
G
B
R
G
B
7
8
2
4
3
Collision logic
B -G
B-B
B-A
A-R
A-G
A-B
0
0
0
2
0
2
R
7
Diversity
Predators
Movement logic
G
B
R
G
B
10
0
2
4
2
Collision logic
B -G
B-B
B-A
A-R
A-G
A-B
1
0
2
1
0
2
R
0
Predators
Movement logic
G
B
R
G
B
17
0
4
4
4
Collision logic
B -G
B-B
B-A
A-R
A-G
A-B
1
1
2
1
0
2
R
0
13
R-R
0
R-G
1
R-B
2
R-R
-1
G-G
0
B-B
-1
R-R
1
R-G
2
R-B
0
R-R
-1
G-G
-1
B-B
0
Collision logic
R-A
G-R
G-G
1
2
0
Scoring logic
A-R
A-G
A-B
1
0
-1
Collision logic
R-A
G-R
G-G
0
0
1
Scoring logic
A-R
A-G
A-B
0
-1
-1
G-B
2
G-A
0
B-R
1
G-R
0
B-R
-1
B-G
-1
G-B
2
G-A
2
B-R
1
G-R
-1
B-R
1
B-G
0
R-R
1
R-G
2
R-B
1
R-R
1
G-G
1
B-B
-1
R-R
2
R-G
0
R-B
2
R-R
-1
G-G
0
B-B
0
Collision logic
R-A
G-R
G-G
1
0
0
Scoring logic
A-R
A-G
A-B
0
1
-1
Collision logic
R-A
G-R
G-G
2
0
0
Scoring logic
A-R
A-G
A-B
0
1
1
G-B
2
G-A
0
B-R
2
G-R
-1
B-R
1
B-G
0
G-B
2
G-A
1
B-R
2
G-R
1
B-R
1
B-G
0
(a)
(b)
(a)
(b)
Artificial Intelligence based Automatic generation of Entertaining Gaming Engines
Usability
Predators
Movement logic
G
B
R
G
B
18
19
1
2
2
Collision logic
B -G
B-B
B-A
A-R
A-G
A-B
2
2
0
2
0
2
R
20
Predators
Movement logic
G
B
R
G
B
20
20
4
4
2
Collision logic
B -G
B-B
B-A
A-R
A-G
A-B
2
0
2
2
0
2
R
19
R-R
2
R-G
0
R-B
0
R-R
1
G-G
-1
B-B
0
R-R
2
R-G
0
R-B
2
R-R
-1
G-G
-1
B-B
1
Collision logic
R-A
G-R
G-G
2
2
1
Scoring logic
A-R
A-G
A-B
1
0
0
Collision logic
R-A
G-R
G-G
2
2
2
Scoring logic
A-R
A-G
A-B
-1
1
1
G-B
2
G-A
0
B-R
2
G-R
-1
B-R
0
B-G
0
G-B
0
G-A
2
B-R
2
G-R
0
B-R
-1
B-G
-1
(a)
(b)
Combined Fitness
Predators
Movement logic
G
B
R
G
B
8
3
1
1
0
Collision logic
B -G
B-B
B-A
A-R
A-G
A-B
2
2
0
0
0
0
R
15
Predators
Movement logic
G
B
R
G
B
20
3
2
4
2
Collision logic
B -G
B-B
B-A
A-R
A-G
A-B
1
1
2
0
0
2
R
11
14
R-R
1
R-G
2
R-B
0
R-R
1
G-G
1
B-B
-1
R-R
2
R-G
2
R-B
0
R-R
0
G-G
1
B-B
1
Collision logic
R-A
G-R
G-G
0
1
0
Scoring logic
A-R
A-G
A-B
-1
-1
1
Collision logic
R-A
G-R
G-G
2
2
2
Scoring logic
A-R
A-G
A-B
-1
1
0
G-B
1
G-A
2
B-R
0
G-R
-1
B-R
-1
B-G
-1
G-B
0
G-A
1
B-R
1
G-R
-1
B-R
0
B-G
1
(a)
(b)
Artificial Intelligence based Automatic generation of Entertaining Gaming Engines
Controller Learning Ability
Random
Combined-ANN
Combined-RB
Usability-ANN
Challenge-ANN
Duration-ANN
Diversity-ANN
Usability-RB
Challenge-RB
Duration-RB
Diversity-RB
0
200
400
600
800
1000
1200
Diversity- Duration- Challeng Usability- Diversity- Duration- Challeng Usability- Combine Combine
Random
RB
RB
e-RB
RB
ANN
ANN
e-ANN
ANN
d-RB
d-ANN
No. Of Iterations
3
5
6
1000
71
1000
1000
64
320
310
63
15
Artificial Intelligence based Automatic generation of
Human User Survey
ANN BasedDuratio
Controller
•User Survey
•
•
10 subjects
Conducted in two different sets on
different days
•
•
•
•
12
10
8
6
4
2
0
16
n
4%
Rando
m
0%
Rule based controller
ANN based controller
Each individual was given 6 games
Play 2 times
Rule Based
Controller
ANN Based
Controller
Combin
ed
Fitness
40%
Challen
ge
32%
Usability
24%
Random
0%
Diversity
0%
Human User Survey
Rule Based Controller
Duration
12%
Combine
d Fitness
47%
Challeng
e
23%
Usability
18%
Diversity
0%
Artificial Intelligence based Automatic generation of
Thank you for your patience
Questions
This presentation is uploaded at
http://ming.org.pk/zahid.htm
Bibliography
•
Halim, Zahid, A. Rauf Baig, and Hasan Mujtaba. "Measuring entertainment and automatic generation of entertaining
games." International Journal of Information Technology, Communications and Convergence 1.1 (2010): 92-107.
•
Halim, Zahid, A. Rauf Baig, and Mujtaba Hasan. "Evolutionary Search For Entertainment In Computer Games." Intelligent
Automation & Soft Computing 18.1 (2012): 33-47.
•
Halim, Zahid, and A. Raif Baig. "Evolutionary Algorithms towards Generating Entertaining Games." Next Generation Data
Technologies for Collective Computational Intelligence. Springer Berlin Heidelberg, 2011. 383-413.
•
J.Schmidhuber, ”Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts”, Connection
Science, vol. 18, pp.173–187, 2006
•
N. Esposito, “A Short and Simple Definition of What a Videogame Is”, in proceedings of Digital Games Research
Association (DiGRA), Vancouver, Canada, 16-20 June, 2005
•
J.Smed and H.Hakonen, "Towards a Definition of a Computer Game", Technical Report, Computer Games Research
Group, Department of Information Technology, University of Turku, Finland, 2005
•
http://www.glasbergen.com/computer-cartoons/
•
http://www-formal.stanford.edu/jmc/whatisai/node1.html
•
G. N. Yannakakis , J. Hallam, ”Towards Optimizing Entertainment In Computer Games”, Applied Artificial Intelligence”,
v.21 n.10, p.933-971, November 2007
•
http://www.easy-marketing-strategies.com/writing-a-case-study.html
•
http://www.123rf.com/photo_9926111_confused-cartoon-guy.html
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Artificial Intelligence based Automatic generation of Entertaining Gaming Engines