This document summarizes Joseph Krall's PhD defense presentation on his theory of fun in video games. The presentation covered several topics: dimensions of fun including originality, gameplay and story; believable AI; procedural content generation; a theory of fun involving stages of gameplay and maintaining playability and replayability; methods for studying games including surveys; and conclusions about using empirical research and a theory of fun to help game developers create more enjoyable games.

1. The Theory of Fun
Qualifier PhD. Defense Presentation
Joseph Krall

2. People play games to reach a certain state of mind
- Defining that state of mind
- Describing how we enter that state of mind
- Theories of how to keep players there
- This benefits the Video Game Industry
Overview
10/26/2012 2Games and Software Engineering
Making a Statement

3. This is a Qualifier Presentation
So here’s some topics about stuff we did
– Dimensions of Fun
– Believable AI
– Procedural Content Generation
– Designing Games
– Our Theory of Fun
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Overview

4. Overview
1. Dimensions of Fun
2. Believable AI
3. Procedural Content Generation
4. A Theory of Fun
5. Playability
6. Replayability
7. Our Method of Studying Games
Conclusion
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Table of Contents

5. • What is Fun? An early theory
– Originality
– Gameplay
– Story
– Replayability
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1. Dimensions of Fun

6. • Originality
– Uniqueness of names
– Theory: More syllables = more unique
– Theory: More originality = more fun
• Why?
– Unique stuff easier to remember
– Experiences made more memorable
– Experience = one aspect of replayability
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1. Dimensions of Fun

7. • Gameplay
– Gameplay = Playability
– Effective rewards/punishment system
– Fluidity of Interface between player and game
• Why?
– Bad gameplay = player quits
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1. Dimensions of Fun

8. • Story
– Amount of player interaction with the game
– Short term vs. long term goals
• What is better?
– More story? Narrative games
– Less story?
– They are just two different kinds of games
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1. Dimensions of Fun

9. • Replayability
– Additional ways to play (Impact)
– Additional post-game content (Completion)
• Goals?
– More replay = play more
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1. Dimensions of Fun

10. Screenshot of Game
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1. Dimensions of Fun

11. Overview
1. Dimensions of Fun
2. Believable AI
3. Procedural Content Generation
4. A Theory of Fun
5. Playability
6. Replayability
7. Our Method of Studying Games
8. Conclusion
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Table of Contents

12. 10/26/2012 Games and Software Engineering 12
2. Believable AI

13. • Turing’s Test
– Is it a computer or a human?
• Believability in Games
– Preserving the “Magic Circle”
– Limit Distractions (High Playability)
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2. Believable AI

14. • Believable AI
– Realistic Behavior
• Expectations of Believability
– Are Low
– Expectations rise over time
• (Same with graphics)
• (… and sounds, and more)
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2. Believable AI

15. Overview
1. Dimensions of Fun
2. Believable AI
3. Procedural Content Generation
4. A Theory of Fun
5. Playability
6. Replayability
7. Our Method of Studying Games
8. Conclusion
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Table of Contents

16. • What is PCG?
– Procedural Content Generation
– Automated generation of content
• Why use PCG?
– Game with static content gets boring
– Keep a game fresh, add replayability
– Assist the game designer
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3. PCG

17. • Many kinds of PCG
– We investigate run-time PCG “level generation”
• Dungeon Generation
– Maze Generation Algorithms
• (Dungeons are mazes with rooms)
– We use: BSP Tree Algorithm
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3. PCG

18. • AiMazed2D Game
– Build a dungeon with BSP Tree Algorithm
– Add objectives
– Solve it automatically
• AI Solver Agent
– Movement through dungeon in human manner
– Choice Points Survey to learn human manner
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3. PCG

19. • Choice Points Survey
– All possible choices in a dungeon
– Results: %’s of each choice
• AI Solver Agent Algorithm
– Our own intuition: “Darkway Algorithm”
– Humanly Learn dungeon layout
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3. PCG

20. 10/26/2012 Games and Software Engineering 20

21. • Qubey’s Deep Dungeon – New AiMazed2D
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3. PCG

22. Overview
1. Dimensions of Fun
2. Believable AI
3. Procedural Content Generation
4. A Theory of Fun
5. Playability
6. Replayability
7. Our Method of Studying Games
8. Conclusion
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Table of Contents

23. • Three Components of fun:
• Advertising & Marketing
• Playability
• Replayability
• Stages of Game Play:
• What players go through cognitively
• First Glance – First Play – Game Play – Quit
• Time Stream
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4. Theory of Fun

24. • Advertising: what/who/when
– Generate hype
• Marketing: how/where
– Know your target audience(s)
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4. Theory of Fun

25. • Playability
– Limiting Distractions
– What not to do
– Section 5
• Replayability
– How long until we get bored?
– What to do
– Section 6
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4. Theory of Fun

26. Stages of Game Play
• Stage I: First Glance
– Do you buy the game or not?
• Yes: move on to next stage
• No: move to time stream
– Advertising/Marketing
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4. Theory of Fun

27. • Stage II: First Play
– First Experience vs. Expectations
• Yes: move on to next stage
• No: move to Time Stream
– Low Playability?
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4. Theory of Fun

28. • Stage III: Game Play
– Goal: Get here and stay
• Replayability
– When they quit:
• Before “long”: game got boring
– Move to time stream (bad)
• After “long”: game was exhausted
– Advance to final stage (good)
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4. Theory of Fun

29. • Stage IV: Quit
– Congratulations if you get here
– Now go to the time stream
• Time Stream
– A place to be when not playing
– Game may become interesting again
– Makes entertainment a cycle
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4. Theory of Fun

30. Overview
1. Dimensions of Fun
2. Believable AI
3. Procedural Content Generation
4. A Theory of Fun
5. Playability
6. Replayability
7. Our Method of Studying Games
8. Conclusion
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Table of Contents

31. • Playability
– Expectations
– Immersion
– Distraction
– Categorize the Distractions
• Functional, Structural, Audiovisual, Social
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5. Playability

32. • Functional Playability
– System performance
– Interface fluidity
• Structural Playability
– Too Easy < Optimal flow state < Too Difficult
– Progression
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5. Playability

33. • Audiovisual Playability
– Graphics/Sounds
– Escalation of Expectation
• Social Playability
– Playing with/against Others
– Don’t provide multiplayer if you can’t
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5. Playability

34. Overview
1. Dimensions of Fun
2. Believable AI
3. Procedural Content Generation
4. A Theory of Fun
5. Playability
6. Replayability
7. Our Method of Studying Games
8. Conclusion
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Table of Contents

35. • Replayability
– Permanence of one’s Willingness to Immerse
• What keeps a game from becoming boring
– Six “Aspects of Replayability” = SChEMICo
• Social, Challenge, Experience, Mastery, Impact, Completion
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6. Replayability

36. • Social Replayability
– We play for social reasons
– Friends
– Conversation
• Challenge Replayability
– We play for accomplishments
– Bragging Rights
– Zillmans’ Excitation Transfer Theory
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6. Replayability

37. • Experience Replayability
– Uniqueness of a game appeals to us
– We play for nostalgia
• Mastery Replayability
– We play to become the best
– Competition drives us
– Goals drive us
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6. Replayability

38. • Impact Replayability
– We play for “impact”
– Sense of Free Will in games
– Play the game different ways
• Completion Replayability
– Goal of doing everything
– Achievements and goal driven
– Story Driven!
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6. Replayability

39. Overview
1. Dimensions of Fun
2. Believable AI
3. Procedural Content Generation
4. A Theory of Fun
5. Playability
6. Replayability
7. Studying Games
8. Conclusion
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Table of Contents

40. • JSEA Paper
– Describe how/why to study games
• Surveys
– Gaming Datasets about Replayability
– Analysis & Ecological Effects
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7. Studying Games

41. • Describing the “Game Space”
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7. Studying Games

42. • Previous Gaming Data
– Fratessi et al: “All Games”
• Gaming Data we gathered:
– “All Board Games”
– “Settlers of Catan” (SOC)
– “Fly for Fun” (FlyFF)
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7. Studying Games

43. • Our Surveys
– We devised a standard survey (for reproducibility)
– 1. Basic Demographics Section
– 2. Replayability Section
• For each aspect: rate how much you agree with the statement
that you play the game for this aspect (5 point Likert scale)
– 3. Core versus Casual Section
• For both core & casual: rate how much you agree that this
game is a core/casual game (5 point Likert)
• (further categorizes types of games)
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7. Studying Games

44. • How we produce an analysis
– Want Ecological Effects between two groups (of games)
• Share rules of the whole with the parts
– Stats: ANOVA & Tukey HSD
– Lead to JDK Diagrams
– And JDK Reports
– Similar graphs =
ecological effects
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7. Studying Games

45. • And the results…
– SOC is similar to All-BG
– What works for All-BG works for SOC
• A game design methodology
– 1. List out features in the game
– 2. Score the aspects
– 3. Average the scores
– 4. Determine the game’s classification
– 5. Lookup median scores for that class
– 6. Adjust the game’s features to meet median scores
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7. Studying Games
SOC FlyFF Frattesi All-BG
SOC - 33% 17% 40%
FlyFF - 17% 36%
Frattesi - 43%
All-BG -

46. Overview
1. Dimensions of Fun
2. Believable AI
3. Procedural Content Generation
4. A Theory of Fun
5. Playability
6. Replayability
7. Studying Games
8. Conclusion
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Table of Contents

47. • We want to make games better
– But how/why?
– Cognitive Exercise
• Software Engineering principles
– Empirical Research
– Data Mining
– Engineering Methodologies
• A Theory of Fun
– Describing Fun and what players want
– Give developers guidelines what to do/what not to do
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8. Conclusion

48. • Game Over. Play again?
– Questions?
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The End