Slides presenting the paper TOG: An Innovation Centric Approach to teaching Computational Expression and Game Design by Mirjam P Eladhari at Teaching Games: Pedagogical Approaches - DiGRA 2019 Pre-Conference Workshop (TGPA:DiGRA2019) August 6 2019, Kyoto, Japan.

1. TOG: AN INNOVATION CENTRIC APPROACH TO TEACHING
COMPUTATIONAL EXPRESSION AND GAME DESIGN
MIRJAM PALOSAARI ELADHARI, SÖDERTÖRN UNIVERSITY
TEACHING GAMES: PEDAGOGICAL APPROACHES - DIGRA 2019 PRE-CONFERENCE WORKSHOP (TGPA:DIGRA2019) AUGUST 6 2019, KYOTO, JAPAN.

2. OVERVIEW
➤ Intro
➤ Background: AIGD and TOG approach
➤ Case study:The course
➤ Knowledge gathering phase
➤ Development phase
➤ Reflection phase
➤ Lessons Learned

3. COURSE FACTS
➤ 5 ECTS given over one semester. (2013 and 2014)
(1 ECTS = 25 hours student work. One semester = 30 ECTS)
➤ Students each
➤ gave 2 - 3 text presentations,
➤ made one game,
➤ wrote a report and defended it.
➤ 8 seminars
➤ 6 workshops
➤ 3 guest lectures

4. Technology
Ex: AI approaches or emerging
interfaces/hardware
Game Genre
Design conventions in
different game genres
Ontology as Metaphor
Theories, Subjects, System
s
TOG: TECHNOLOGY,
ONTOLOGY AND GAME GENRE

5. WHY TOG IN TEACHING?
➤ to enable students to expand their palette as game
designers - adding AI approaches and emerging interfaces
and hardware.
➤ an expanded tech-palette is empowering - but it also
shows how vast the design space is - lots of uncharted
territory. Room for innovation.
➤ the tech-palette can be composed to be useful for both
students who program and for those who won't.
WHY?

6. WHY TOG IN TEACHING?
➤ to enable students to expand their palette as game
designers - adding AI approaches and emerging interfaces
and hardware.
➤ an expanded tech-palette is empowering - but it also
shows how vast the design space is - lots of uncharted
territory. Room for innovation.
➤ the tech-palette can be composed to be useful for both
students who program and for those who won't.
to enable students to
expand their palette as
game designers - adding
AI approaches and
emerging interfaces and
hardware.
WHY?

7. WHY TOG IN TEACHING?
➤ to enable students to expand their palette as game
designers - adding AI approaches and emerging interfaces
and hardware.
➤ an expanded tech-palette is empowering - but it also
shows how vast the design space is - lots of uncharted
territory. Room for innovation.
➤ the tech-palette can be composed to be useful for both
students who program and for those who won't.
to enable students to
expand their palette as
game designers - adding
AI approaches and
emerging interfaces and
hardware.
WHY?
an expanded tech-
palette is
empowering - but it
also shows how vast
the design space is -
lots of uncharted
territory. Room for
innovation.

8. WHY TOG IN TEACHING?
➤ to enable students to expand their palette as game
designers - adding AI approaches and emerging interfaces
and hardware.
➤ an expanded tech-palette is empowering - but it also
shows how vast the design space is - lots of uncharted
territory. Room for innovation.
➤ the tech-palette can be composed to be useful for both
students who program and for those who won't.
to enable students to
expand their palette as
game designers - adding
AI approaches and
emerging interfaces and
hardware.
WHY?
an expanded tech-
palette is
empowering - but it
also shows how vast
the design space is -
lots of uncharted
territory. Room for
innovation.
the tech-palette
can be composed
to be useful for
both students
who program
and for those
who won't.

9. BACKGROUND: AIGD

10. BACKGROUND: AI BASED GAME DESIGN
When the AI is central to the game design, and in the
foreground.

11. Diagram is a joint effort of Josh McCoy, Anne Sullivan, Gillian Smith, and me (2011, Santa
Cruz)

12. Diagram is a joint effort of Josh McCoy, Anne Sullivan, Gillian Smith, and me (2011, Santa
Cruz)

13. Technology
Ex: AI approaches or emerging
interfaces/hardware
Game Genre
Design conventions in
different game genres
Ontology as Metaphor
Theories, Subjects, System
s
TOG: TECHNOLOGY,
ONTOLOGY AND GAME GENRE

14. CASE STUDY:
THE COURSE

15. COURSE: COMPUTATIONAL EXPRESSION
➤ Masters’ level course, 5 ECTS.
➤ Given at the Institute of Digital
Games at the University of Malta
➤ Majority of students were
programmers.
➤ Students had prior game design
knowledge
➤ Text seminars
➤ Workshops
➤ Guest lectures
➤ Game prototyping
➤ Reflection
+ papers
& articles

16. COURSE TIME LINE

17. KNOWLEDGE GATHERING
PHASE

18. PALETTE - THE SEMINAR TOPICS
- Computational Expression, Software
studies and platform studies
- The relation between Game Design
and Artificial Intelligence, AI Based
game design
- Approaches and technologies for
Interactive Narrative
- Characters, Characterisation and
Agents in computer games
- Procedurally Generated Content and
data mining
- Biofeedback as player
modelling and/or game
interface
- Interface technologies beyond
the personal computer
(CAVEs, Augmented Reality,
Pervasive, Console stereoscopic
camera systems etc.)
- Computational Creativity
- Artificial Life

19. TEXT SEMINARS, CHAMPIONED

20. GUEST LECTURERS (2014) - SHOWING PRACTICE
➤Richard Evans on narrative in Versu,
and BDI in Black&White
➤Gillian Smith on PCG - The Endless
Web
➤Brian Magerko on improvisation and
agents - Viewpoints AI
➤Noah Wardrip-Fruin on expressive
processing

21. WORKSHOPS - TRYING HANDS-ON
➤Trying out hardware and software
hands on (in pics: STRIPS,
Emotive&NeuroSky, ELIZA)
Each workshop had alternative tools requiring more or less coding.

22. DEVELOPMENT PHASE

23. Technology
Ex: AI approaches or
emerging interfaces/
hardware
Game
Genre
Design
conventions
Ontology as
Metaphor
Theories, Subjects,
System
s
TASK: (KEPT IN MIND DURING KNOWLEDGE GATHERING PHASE)
Make a playable prototype
using a central AI or
technology, using
a rich metaphor as
inspiration for the
game mechanics,
while being aware
of any genre
conventions used
(or not).
The Triad

24. STUDENTS’ DEV PROCESS
➤ Conceptualisation
➤ Prototyping
➤ Play-testing &
Iteration
➤ Reflection
➤ Finalising &
polishing

25. EXAMPLE PROJECTS

26. HAIWAICODE
Racing
Car trafficBy Vincent Farrugia and Alan
Pirotta
Machine
learning

27. COMPOBLOCKS
Platformer
M
usic
com
positionn
By Luke Aquilina and Karl
Grech
PCG

28. COMPOBLOCKS
Platformer
Music
compositio
By Luke Aquilina and Karl Grech
Machine learning

29. ORGANATRON
Strategy
Robot
wars
By Noel Cuschieri
and Matthew Agius
PCG, Genetic
Algoritms

30. ORGANATRON
Strategy
Robot
wars
By Noel Cuschieri and Matthew Agius
PCG, Genetic

31. DUNGEONS & MAYBE DRAGONS
Dungeon crawlers
By Jean-Luc Portelli
and Andrea Piano
PCG &
Quest flags
RPG & Game
mastering

32. HERAKLES
Shooter
Greek
MythBy Stelios Avramidis, Joseph
Darmanin and Michael
Camilleri
Gyro

33. LINE
YellowtailM
inim
alism
(art)
By David Chircop and Gary Hili
Ipad finger
input

34. LINE
Yellowtail
Minimalis
m (art)
By David Chircop and Gary Hili
Ipad finger input

35. LINE
Yellowtail
Minimalis
m (art)
By David Chircop and Gary Hili
Ipad finger input

36. LINE
Yellowtail
Minimalis
m (art)
By David Chircop and Gary Hili
Ipad finger input

39. REFLECTION PHASE

40. REFLECTING AND REPORTING
Development stop ->
time for reflection and
reporting.
Writing, sending text
forward in reading
chain. Text seminar
Computational Expression in own practice, and the future of the field

41. REFLECTION PHASE
➤ How did the technology affect the design?
➤ How did the design affect the technology?
➤ If you used a genre, a hybrid genres, or no genre - how did
that affect your design and your approach to the technology
used?
➤ How did the knowledge domain or theory used affect the
design?
➤ What other types of expressive computational technologies
could have been useful for realizing your design or core
idea? Reflect on whether use of those would have had
impact on your design, and if so, how.

42. LESSONS LEARNED

43. SUMMARY & LESSONS LEARNED
➤ Groups who put a strong focus on the Ontology (metaphor, the knowledge
domain), in addition to the AI/tech, generally created the more interesting
and innovative designs.
➤ Combination of project work and classic seminar defense style teaching
allowed both concrete prototypes and reflection.
➤ Outcomes for students:
➤ games for portfolios
➤ seeds for research papers
➤ seeds for thesis topics
➤ proof of concepts:feasibility for thesis work or game-to-market
➤ (more) realistic views on dev effort for custom AI
➤ expanded palette for innovation and development in future career

44. THANK YOU FOR LISTENING!
QUESTIONS?
Mirjam Palosaari Eladhari
Mirjam.palosaari.eladhari@sh.se
Download course materials here:
https://sites.google.com/site/
computationalexpression/