Elucidation of Fun of Games

Structured IRF Model and Automated Game Design

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IDO Satoshi - @kan_jiro

MDA Framework (Hunicke et al., 2004)– Describes the structure which consists of 3 classes

of Mechanics, Dynamics, and Aethetics

– Does not classify Aesthetics logically.

Structured IRF Model– Classifies factors of fun into influence, reward, and fictionality

and combines them with the objectives structure of the whole game

– Describes almost kinds of fun of games such as dilemma, action, and narrative, in one theoretical framework

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Existing Theory and Structured IRF Model

1. What is a Game?– The new definition of game solves the boundary problems

by distinction between a game and gameplay

2. What is Fun of Games?– IRF Framework classifies gameplay into influence, reward, and fictionality

3. How Game Structure can be described?– Structured IRF model combines gameplay with the objectives structure

4. Game Design Process in the Near Future– Most of game design process would be automated in the near future

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Structure of This Session

1.

What is a Game?

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Is a math exam a game?– The fun from solving difficult problem by trial and error

is similar to the one from games

– If a math exam is not a game, for what reason?

Is tic-tac-toe a game?– If the both players know the best process to play,

it would be a simple work

– If tic-tac-toe is a game, for what reason?

Boundary between Game and Non-game

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Active Behavior– A behavior influencing some objects

aimed at achieving some objective

– e.g. work, study, and a game

Gameplay– Fun from active behaviors

Game– A system designed in order

to generate great gameplay with small pain and labor

Definition of Gameplay and Game

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A math exam is not a game– Gameplay can be generated

when the examinee influence the difficult problem to be solved

– Even so, a math exam is not a game because it is not a system designed in order to generate gameplay

Tic-tac-toe is a game– Gameplay cannot be generated if the player knows the best process and

wants to win, because there is no room for influencing by the player

– Even so, tic-tac-toe is a game because it is a system designed in order to generate gameplay

Answer to Boundary Problem

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2.

What is Fun of Games?

Fundamental Classification of Gameplay

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Influence– Feeling of having strong influence

on the game

Reward– Attraction of the situation

which is brought by achieving the objective

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Classification of Influence

Interaction– Operation to in-game objects by the

player his present self, and the feedback

Communication– Transmission thoughts or feeling

aimed at affecting other players’ influence on in-game objects

Strategy– Planning aimed at affecting the player

his future self’s influence on in-game objects

Major Interaction

Interaction of Operation– Players experience pleasant feelings in the operating the object itself

– e.g. platformer

Interaction of Spread– The influence from the operation spreads to many things

directly or indirectly

– e.g. physic puzzle

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Classification of Communication

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Oppositional Communication–With the players which must or may be opponents

The cases where there are the information gaps are good illustration

– e.g. poker

Friendly Communication–With the players which must not be opponents

– e.g. pen-and-paper RPG

Classification of Strategy

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Gameful Strategy– The player is set clear objectives

The player aims at achieving given objectives

– e.g. chess

Toyful Strategy– The player is set vague or extremely mild objectives

The player aims at achieving objectives of his own accord

– i.e. sandbox

Major Reward – 1/2

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Reward of Solution– Solving instability, opacity, or lack– e.g. treasure hunting and puzzle (complex one contain strategy)

Reward of Destruction– Destructing some objects– e.g. shooter

Major Reward – 2/2

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Reward of Praise– Taking pride in about the achievements or self-absorbing– e.g. social network game and rhythm game

Reward of Growth– Enhancing the player’s own influence– e.g. level-up in RPG

Reward of Benefit– Getting benefit for a real life– e.g. brain training

Game Mechanics and Fictional World

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Game Mechanics– Game mechanics mean rules in a broad sense

Contains implementations by programming and physical laws

– Influence and reward result from interest in game mechanics

Fictional World– e.g. “The Earth is being attacked by aliens”

“Luigi is a Mario’s brother”

– The gameplay which result from interest in fictional world is called fictionality

Major Fictionality

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Fictionality of Love– Comparing a object or a process of influence to the player’s favorite

– e.g. caring game, sports game, and game which is set in attractive worlds

Fictionality of Story– Giving meanings in story to the player’s behavior

– e.g. visual novel

Fictionality of Experience– Feeling that the events in fictional world

would be the player’s own experience

– i.e. narrative experience

Summary of Classification of Gameplay

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Gameplay from Game Mechanics Influence

Interaction– Interaction of Operation– Interaction of Spread

Communication– Oppositional Communication– Friendly Communication

Strategy– Gameful Strategy– Toyful Strategy

Reward– Reward of Solution– Reward of Destruction– Reward of Growth– Reward of Praise– Reward of Benefit

Gameplay from Fictional WorldFictionality

– Fictionality of Love– Fictionality of Story– Fictionality of Experience

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3.

Where Does each Fun Come from?

Kinds of Objectives

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Main Objective– The goal of one playing which is in mind when he joins the game

– e.g. In mahjong, going to the top in the game

Clear Objective–When the process starts and ends is clear

– e.g. clearing a level in platform games

Vague Objective–When the process starts and ends is not clear– e.g. getting over a barrier in the level in platform games

Relationships between Objectives

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Lower and Upper Integration– Repetitive achieving the lower objectives results

in one doing the upper objective– lower objective A1 is completed lower objective A2 is completed … ∧ ∧

= upper objective A is completed

– e.g. In mahjong, going to the top in the game and earning more scores in each round

Former and Later Integration– Achieving the former objective

is the necessary condition for doing the later objective – former objective A is completed later objective B is completed⊂– e.g. In TCGs, making a more powerful deck and winning the match

Integration of Progression or Emergence

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Integration of Progression– The evaluation function of lower or former objectives

for achieving the upper or later objective is simple

– e.g. In mahjong, the relationship between one round and one game

The criterion of results of each round for being at the top in the game is almost the difference in score between himself and the top player

Integration of Emergence– The evaluation function of lower or former objectives

for achieving the upper or later objective is complex

– e.g. In mahjong, the relationship between one turn and one round

The criterion of results of each turn for reducing the difference in score between himself and the top player is complex

Progression / Emergence and Gameplay

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The advantage of Integration of Progression– The feedback can be given immediately and clearly

Reward can be promoted

The advantage of Integration of Emergence– Influence can be promoted

Especially, it is necessary for strategy

Analysis Sample: Super Mario Brothers

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Analysis Sample: The Legend of Zelda

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Analysis Sample: POKeMON

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Analysis Sample: Puyo Puyo

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Analysis Sample: Brain Age

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Analysis Sample: Earthbound

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Function of Objectives

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Classification of Major Function of Objectives① Main objective

② The objective combining directly with gameplay (core objective)

③ The objectives compose the integration combining directly with gameplay (core integration)

④ The objectives compose the integration contributed by reward of growth

⑤ Upper objective for prolonging the life of fun of main objective (meta objective)

⑥ Objective for dividing too much lower objectives per one upper objective

Vary the pace

Combination of IRF and Existing Frameworks

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EMS Framework (Nakamura, 2014)– Describes games

by the Ends and Means Structure

– Enables beginners of game design to come up with ideas

MDA Framework– Describes games by the structure

which consists of Mechanics, Dynamics, and Aesthetics

– Facilitates giving shape of concrete algorithm and data to abstract fun which intended to give players

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Game Design Process in the Near Future

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Automated Game Design (AGD)

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Automated Game Design– Difficulty Balancing, level design, and generating game mechanics by AI

– Research in AGD has developed since around 2005

AGD based on IRF Framework– Quantifies each gameplay

– Creates the game generates more gameplay by evolutionary algorithm

The order of difficulty of quantification– Strategy < Communication << Interaction < Reward <<< Fictionality

– Strategy and communication almost can be quantified by existing technique

Automated design of chess problems1. Enumeration of chess problems by the retrograde method

2. Having AI solve the problems

3. Extraction of the problems have large strategy

Quantification of Strategy– Strategy is the difference between

predicted states of the game from several considered move;

– the distance between dots of each state plotted at high-dimensional coordinates based on each member of the evaluation function

Design of Chess Problems by AGD

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Creating Game Mechanics by AGD

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Yavalath (Browne, 2007)– Is the first commercial game

created by AGD from start to finish

– Is rated highly by board game fans

LUDI - AGD system, which created Yavalath

– Uses evolutionary algorithm have the factors of existing combinatorial game mechanics (e.g. Go, Reversi, and Gomoku) be the genes

–Measures the fitness by self-play and 57 criteria

– Creates Gomoku-like games for the greater part as its shortcoming

Feasibility of General AGD

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Is AGD creates every kind of games feasible?– The general AGD needs criteria and automatic players

for every kinds of games

They can be generated by deep learning in the near future

Phases of General AGD - 1/2

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0th phase| Automated test play– AGD learns the given game mechanics through automated playing

– AGD reports the situation which is differ greatly from the one which AI predicted (may results from a bug)

0.5th phase | Quantification of gameplay– AGD values amount of change of strategy or communication

which is generated from the parameter adjustment by humans

1st phase | Automated input of parameters– AGD adjusts parameter and position to maximize

strategy and communication based on the given assets

Phases of General AGD - 2/2

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2nd phase | Automated level design– AGD quantifies all gameplay from game mechanics

– AGD creates additional game mechanics, items, skills, enemies, and levels based on given fundamental game mechanics

– Humans choose which level to be implemented and assign meaning on fictional world (e.g. forest, dessert)

3rd phase | Automated game mechanics design– AGD creates all game mechanics from scratch

– Humans choose which game mechanics to be implementedand assign meaning on fictional world (e.g. collecting monsters, governing countries)

4th phase | Automated whole game design

Human Game Designers at AGD Era

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Operation of AGD for individual games–What kind of assets should be AGD given? What should AGD creates?

– Humans must value created contents and educate AGD

Generating Fictionality– AGD cannot creates fictionality with high accuracy

only by simple pattern recognition because it depends on contexts

Enriching contents– However, the disposable time of players is limited

Refining of taste only by human designers– It is expected that contents AGD created have peculiar tastes

Summary

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Definition of a game– A game is the system aimed at generating gameplay

Classification of gameplay– Gameplay can be classified into interaction, communication, strategy,

reward, and fictionality

Diagramming of a game structure– A game diagram can be described by combining gameplay

with objectives or integration

Automated Game Design– The quantifiability of the kinds of gameplay is uneven, and easily

quantifiable one can be generated automatically in the near future