Qualifier presentation

The Theory of Fun

Qualifier PhD. Defense Presentation Joseph Krall

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 2 Games and Software Engineering

Making a Statement

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

10/26/2012 Games and Software Engineering 3

Overview

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


Table of Contents

• What is Fun? An early theory

– Originality

– Gameplay

– Story

– Replayability



• 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



• Gameplay

– Gameplay = Playability

– Effective rewards/punishment system

– Fluidity of Interface between player and game

• Why?

– Bad gameplay = player quits



• 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



• Replayability

– Additional ways to play (Impact)

– Additional post-game content (Completion)

• Goals?

– More replay = play more



Screenshot of Game



Overview


2. Believable AI


4. A Theory of Fun

5. Playability

6. Replayability


8. Conclusion


Table of Contents


2. Believable AI

• Turing’s Test

– Is it a computer or a human?

• Believability in Games

– Preserving the “Magic Circle”

– Limit Distractions (High Playability)


2. Believable AI

• Believable AI

– Realistic Behavior

• Expectations of Believability

– Are Low

– Expectations rise over time

• (Same with graphics)

• (… and sounds, and more)


2. Believable AI

Overview


2. Believable AI


4. A Theory of Fun

5. Playability

6. Replayability


8. Conclusion


Table of Contents

• 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


3. PCG

• 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


3. PCG

• 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


3. PCG

• 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


3. PCG

• Qubey’s Deep Dungeon – New AiMazed2D


3. PCG

http://aimazed2d-web-dev-env.elasticbeanstalk.com/

Overview


2. Believable AI


4. A Theory of Fun

5. Playability

6. Replayability


8. Conclusion


Table of Contents

• 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


4. Theory of Fun

• Advertising: what/who/when

– Generate hype

• Marketing: how/where

– Know your target audience(s)


4. Theory of Fun

• Playability

– Limiting Distractions

– What not to do

– Section 5

• Replayability

– How long until we get bored?

– What to do

– Section 6


4. Theory of Fun

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


4. Theory of Fun

• Stage II: First Play

– First Experience vs. Expectations

• Yes: move on to next stage

• No: move to Time Stream

– Low Playability?


4. Theory of Fun

• 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)


4. Theory of Fun

• 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


4. Theory of Fun

Overview


2. Believable AI


4. A Theory of Fun

5. Playability

6. Replayability


8. Conclusion


Table of Contents

• Playability

– Expectations

– Immersion

– Distraction

– Categorize the Distractions

• Functional, Structural, Audiovisual, Social


5. Playability

• Functional Playability

– System performance

– Interface fluidity

• Structural Playability

– Too Easy < Optimal flow state < Too Difficult

– Progression


5. Playability

• Audiovisual Playability

– Graphics/Sounds

– Escalation of Expectation

• Social Playability

– Playing with/against Others

– Don’t provide multiplayer if you can’t


5. Playability

Overview


2. Believable AI


4. A Theory of Fun

5. Playability

6. Replayability


8. Conclusion


Table of Contents

• 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


6. Replayability

• Social Replayability

– We play for social reasons

– Friends

– Conversation

• Challenge Replayability

– We play for accomplishments

– Bragging Rights

– Zillmans’ Excitation Transfer Theory


6. Replayability

• 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


6. Replayability

• 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!


6. Replayability

Overview


2. Believable AI


4. A Theory of Fun

5. Playability

6. Replayability

7. Studying Games

8. Conclusion


Table of Contents

• JSEA Paper

– Describe how/why to study games

• Surveys

– Gaming Datasets about Replayability

– Analysis & Ecological Effects


7. Studying Games

• Describing the “Game Space”


7. Studying Games

• Previous Gaming Data

– Fratessi et al: “All Games”

• Gaming Data we gathered:

– “All Board Games”

– “Settlers of Catan” (SOC)

– “Fly for Fun” (FlyFF)


7. Studying Games

• 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)


7. Studying Games

• 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


7. Studying Games

• 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


7. Studying Games

SOC FlyFF Frattesi All-BG

SOC - 33% 17% 40%

FlyFF - 17% 36%

Frattesi - 43%

All-BG -

Overview


2. Believable AI


4. A Theory of Fun

5. Playability

6. Replayability

7. Studying Games

8. Conclusion


Table of Contents

• 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


8. Conclusion

• Game Over. Play again?

– Questions?


The End

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