ITU

Modeling and Generating Strategy Games Mechanics

Research output: Book / Anthology / Report / Ph.D. thesisPh.D. thesisResearch

Standard

Modeling and Generating Strategy Games Mechanics. / Mahlmann, Tobias.

IT-Universitetet i København, 2013. 194 p. (ITU-DS; No. 91).

Research output: Book / Anthology / Report / Ph.D. thesisPh.D. thesisResearch

Harvard

Mahlmann, T 2013, Modeling and Generating Strategy Games Mechanics. ITU-DS, no. 91, IT-Universitetet i København.

APA

Mahlmann, T. (2013). Modeling and Generating Strategy Games Mechanics. IT-Universitetet i København. ITU-DS No. 91

Vancouver

Mahlmann T. Modeling and Generating Strategy Games Mechanics. IT-Universitetet i København, 2013. 194 p. (ITU-DS; No. 91).

Author

Mahlmann, Tobias. / Modeling and Generating Strategy Games Mechanics. IT-Universitetet i København, 2013. 194 p. (ITU-DS; No. 91).

Bibtex

@phdthesis{35b3fb925c5f48efbdba4336682f14a2,
title = "Modeling and Generating Strategy Games Mechanics",
abstract = "Strategy games are a popular genre of games with a long history, originatingfrom games like Chess or Go. The first strategy games were published as“Kriegspiele” (engl. wargames) in the late 18th century, intended for the education of young cadets. Since then strategy games were refined and transformed over two centuries into a medium of entertainment. Today{\textquoteright}s computer strategy games have their roots in the board- and roleplaying games of the 20th century and enjoy great popularity. We use strategy games as an application for the procedural generation of game content.Procedural game content generation has regained some interest in recentyears, both among the academic community and game developers alike. Whenthe first commercial computer games were published in the early 1980s,technical limitations prevented game developers from shipping their titles witha large amount of pre-designed game content. Instead game content such aslevels, worlds, stories, weapons, or enemies needed to be generated at program runtime to save storage space and memory resources. The generation of game content “on the fly” has gained popularity again for two reasons: first, game production budgets have risen rapidly in the past ten years due to the necessary labour to create the amount of game content players expect in games today. Secondly: the potential audience for games grew in the past years, with adiversification of player types at the same time. Thus game developers look fora way to tailor their games to individual players{\textquoteright} preferences by creating gamecontent adaptively to how the player plays (and likes) a game.W we extend the notion of “procedural game content generation” by “gamemechanics”. Game mechanics herein refer to the way that objects in a game mayinteract, what the goal of the game is, how players may manipulate the gameworld, etc. We present the Strategy Games Description Language (SGDL), atree-based approach to model the game mechanics of strategy games. SGDLallows game designers to rapid prototype their game ideas with the help of ourcustomisable game engine. We present several example games to demonstratethe capabilities of the language and how to model common strategy gameelements.Furthermore, we present methods to procedurally generate and evaluate gamemechanics modelled in SGDL in terms of enjoyability. We argue that anevolutionary process can be used to evolve the mechanics of strategy gamesusing techniques from the field of machine learning. Our results show thatautomated gameplay combined with expert knowledge can be used to determine the quality of gameplay emerging from game mechanics modelled in SGDL, and that algorithms can augment the creativity of human game designers.",
author = "Tobias Mahlmann",
year = "2013",
language = "English",
isbn = "978-87-7949-286-8",
series = "ITU-DS",
publisher = "IT-Universitetet i K{\o}benhavn",
number = "91",
address = "Denmark",

}

RIS

TY - BOOK

T1 - Modeling and Generating Strategy Games Mechanics

AU - Mahlmann, Tobias

PY - 2013

Y1 - 2013

N2 - Strategy games are a popular genre of games with a long history, originatingfrom games like Chess or Go. The first strategy games were published as“Kriegspiele” (engl. wargames) in the late 18th century, intended for the education of young cadets. Since then strategy games were refined and transformed over two centuries into a medium of entertainment. Today’s computer strategy games have their roots in the board- and roleplaying games of the 20th century and enjoy great popularity. We use strategy games as an application for the procedural generation of game content.Procedural game content generation has regained some interest in recentyears, both among the academic community and game developers alike. Whenthe first commercial computer games were published in the early 1980s,technical limitations prevented game developers from shipping their titles witha large amount of pre-designed game content. Instead game content such aslevels, worlds, stories, weapons, or enemies needed to be generated at program runtime to save storage space and memory resources. The generation of game content “on the fly” has gained popularity again for two reasons: first, game production budgets have risen rapidly in the past ten years due to the necessary labour to create the amount of game content players expect in games today. Secondly: the potential audience for games grew in the past years, with adiversification of player types at the same time. Thus game developers look fora way to tailor their games to individual players’ preferences by creating gamecontent adaptively to how the player plays (and likes) a game.W we extend the notion of “procedural game content generation” by “gamemechanics”. Game mechanics herein refer to the way that objects in a game mayinteract, what the goal of the game is, how players may manipulate the gameworld, etc. We present the Strategy Games Description Language (SGDL), atree-based approach to model the game mechanics of strategy games. SGDLallows game designers to rapid prototype their game ideas with the help of ourcustomisable game engine. We present several example games to demonstratethe capabilities of the language and how to model common strategy gameelements.Furthermore, we present methods to procedurally generate and evaluate gamemechanics modelled in SGDL in terms of enjoyability. We argue that anevolutionary process can be used to evolve the mechanics of strategy gamesusing techniques from the field of machine learning. Our results show thatautomated gameplay combined with expert knowledge can be used to determine the quality of gameplay emerging from game mechanics modelled in SGDL, and that algorithms can augment the creativity of human game designers.

AB - Strategy games are a popular genre of games with a long history, originatingfrom games like Chess or Go. The first strategy games were published as“Kriegspiele” (engl. wargames) in the late 18th century, intended for the education of young cadets. Since then strategy games were refined and transformed over two centuries into a medium of entertainment. Today’s computer strategy games have their roots in the board- and roleplaying games of the 20th century and enjoy great popularity. We use strategy games as an application for the procedural generation of game content.Procedural game content generation has regained some interest in recentyears, both among the academic community and game developers alike. Whenthe first commercial computer games were published in the early 1980s,technical limitations prevented game developers from shipping their titles witha large amount of pre-designed game content. Instead game content such aslevels, worlds, stories, weapons, or enemies needed to be generated at program runtime to save storage space and memory resources. The generation of game content “on the fly” has gained popularity again for two reasons: first, game production budgets have risen rapidly in the past ten years due to the necessary labour to create the amount of game content players expect in games today. Secondly: the potential audience for games grew in the past years, with adiversification of player types at the same time. Thus game developers look fora way to tailor their games to individual players’ preferences by creating gamecontent adaptively to how the player plays (and likes) a game.W we extend the notion of “procedural game content generation” by “gamemechanics”. Game mechanics herein refer to the way that objects in a game mayinteract, what the goal of the game is, how players may manipulate the gameworld, etc. We present the Strategy Games Description Language (SGDL), atree-based approach to model the game mechanics of strategy games. SGDLallows game designers to rapid prototype their game ideas with the help of ourcustomisable game engine. We present several example games to demonstratethe capabilities of the language and how to model common strategy gameelements.Furthermore, we present methods to procedurally generate and evaluate gamemechanics modelled in SGDL in terms of enjoyability. We argue that anevolutionary process can be used to evolve the mechanics of strategy gamesusing techniques from the field of machine learning. Our results show thatautomated gameplay combined with expert knowledge can be used to determine the quality of gameplay emerging from game mechanics modelled in SGDL, and that algorithms can augment the creativity of human game designers.

M3 - Ph.D. thesis

SN - 978-87-7949-286-8

T3 - ITU-DS

BT - Modeling and Generating Strategy Games Mechanics

PB - IT-Universitetet i København

ER -

ID: 39500280