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Characteristics of Generatable Games
"... We address the problem of generating complete games, rather than content for existing games. In particular, we try to an-swer the question which types of games it would be realistic or even feasible to generate. To begin to answer the question, we first list the different ways we see that games coul ..."
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Cited by 5 (2 self)
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We address the problem of generating complete games, rather than content for existing games. In particular, we try to an-swer the question which types of games it would be realistic or even feasible to generate. To begin to answer the question, we first list the different ways we see that games could be generated, and then try to discuss what characterises games that would be comparatively easy or hard to generate. The discussion is structured according to a subset of the charac-teristics discussed in the book Characteristics of Games by Elias, Garfield and Gutschera. 1.
Automatic Game Design via Mechanic Generation
"... Game designs often center on the game mechanics— rules governing the logical evolution of the game. We seek to develop an intelligent system that gener-ates computer games. As first steps towards this goal we present a composable and cross-domain represen-tation for game mechanics that draws from AI ..."
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Cited by 3 (1 self)
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Game designs often center on the game mechanics— rules governing the logical evolution of the game. We seek to develop an intelligent system that gener-ates computer games. As first steps towards this goal we present a composable and cross-domain represen-tation for game mechanics that draws from AI plan-ning action representations. We use a constraint solver to generate mechanics subject to design requirements on the form of those mechanics—what they do in the game. A planner takes a set of generated mechan-ics and tests whether those mechanics meet playabil-ity requirements—controlling how mechanics function in a game to affect player behavior. We demonstrate our system by modeling and generating mechanics in a role-playing game, platformer game, and combined role-playing-platformer game.
Open problem: Reusable gameplay trace samplers
- in Proceedings of the AIIDE 2013 Workshop on Artificial Intelligence in the Game Design Process
, 2013
"... We identify an open problem in game design assistance and automation: the development of reusable gameplay trace samplers. Inside many sophisticated content gen-erators and design tools is a component that samples in-teresting and plausible sequences of player actions. De-tails and summary propertie ..."
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Cited by 3 (0 self)
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We identify an open problem in game design assistance and automation: the development of reusable gameplay trace samplers. Inside many sophisticated content gen-erators and design tools is a component that samples in-teresting and plausible sequences of player actions. De-tails and summary properties of these samples are used to assess generated content and to inform designers. As the development of this component is technically in-volved (sometimes comparable to making a second im-plementation of a game’s mechanics), design tools often either make use of entirely custom, game-specific sam-plers or make due without the ability to sample inter-esting traces at all. This severely limits the population who could benefit from automation to those who are motivated to develop it for themselves. We propose the development of reusable samplers to ease the develop-ment of future design automation tools. This paper re-views several systems that demonstrate the availability of technology required by these samplers and the range of applications they may serve. It also sketches how fu-ture samplers might be architected. This proposal iden-tifies one way for technical research to make progress on design automation challenges without making prob-lematic assumptions about the nature of player behavior or designer intent. Filling in this missing infrastructure, we claim, will make the use of artificial intelligence in the design process more accessible and thus accelerate game design projects.
Automatic playtesting for game parameter tuning via active learning
- in 9th International Conference on the Foundations of Digital Games
, 2014
"... Game designers use human playtesting to gather feedback about game design elements when iteratively improving a game. Playtesting, however, is expensive: human testers must be recruited, playtest results must be aggregated and interpreted, and changes to game designs must be extrap-olated from these ..."
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Cited by 2 (1 self)
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Game designers use human playtesting to gather feedback about game design elements when iteratively improving a game. Playtesting, however, is expensive: human testers must be recruited, playtest results must be aggregated and interpreted, and changes to game designs must be extrap-olated from these results. Can automated methods reduce this expense? We show how active learning techniques can formalize and automate a subset of playtesting goals. Specif-ically, we focus on the low-level parameter tuning required to balance a game once the mechanics have been chosen. Through a case study on a shoot-‘em-up game we demon-strate the efficacy of active learning to reduce the amount of playtesting needed to choose the optimal set of game pa-rameters for two classes of (formal) design objectives. This work opens the potential for additional methods to reduce the human burden of performing playtesting for a variety of relevant design concerns. Categories and Subject Descriptors Applied Computing [Computers in other domains]: Per-sonal computers and PC applications—Computer games
Visualizing progressions for education and game design
, 2014
"... Progression design is a critical part of designing games or ed-ucational content. Currently, systems to visualize the content of a progression are limited and do not help designers an-swer questions important to the design process. These ques-tions include comparing two progressions to understand th ..."
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Cited by 1 (1 self)
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Progression design is a critical part of designing games or ed-ucational content. Currently, systems to visualize the content of a progression are limited and do not help designers an-swer questions important to the design process. These ques-tions include comparing two progressions to understand the relative order in which concepts are introduced or how com-plexity changes throughout the progression. We present an interactive visualization system that allows designers to com-pare two different progressions, using multiple views and in-teraction techniques that aim to help designers answer these questions. We evaluate our tool through informal anecdotes, discussing insights that were found on progression data for actively developed games.
Automatic Game Progression Design through Analysis of Solution Features
"... A long-term goal of game design research is to achieve end-to-end automation of much of the design process, one aspect of which is creating effective level progressions. A key diffi-culty is getting the player to practice with interesting combi-nations of learned skills while maintaining their engag ..."
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A long-term goal of game design research is to achieve end-to-end automation of much of the design process, one aspect of which is creating effective level progressions. A key diffi-culty is getting the player to practice with interesting combi-nations of learned skills while maintaining their engagement. Although recent work in task generation and sequencing has reduced this effort, we still lack end-to-end automation of the entire content design process. We approach this goal by incorporating ideas from intelligent tutoring systems and proposing progression strategies that seek to achieve mastery of not only base concepts but arbitrary combinations of these concepts. The input to our system is a model of what the player needs to do to complete each level, expressed as either an imperative procedure for producing solutions or a repre-sentation of features common to all solutions. The output is a progression of levels that can be adjusted by changing high-level parameters. We apply our framework to a popular math puzzle game and present results from 2,377 players showing that our automatic level progression is comparable to expert-crafted progression after a few design iterations based on a key engagement metric.
Abstract Automatic Scaffolding for Procedural Learning
, 2014
"... A key challenge in education is how provide support that is tailored to the learner’s individ-ual needs. Effective teachers and curricula typically provide such support, often referred to as instructional scaffolding, through the development of progressions of practice problems, step-by-step demonst ..."
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A key challenge in education is how provide support that is tailored to the learner’s individ-ual needs. Effective teachers and curricula typically provide such support, often referred to as instructional scaffolding, through the development of progressions of practice problems, step-by-step demonstrations, and strategies for diagnosing misconceptions. This process is often tedious and time-consuming. Furthermore, it typically requires a large amount of design by experts and little can be reused across educational domains. As a result, creating adaptive educational content often remains prohibitively difficult. This thesis presents a general framework for constructing instructional scaffolding for procedural learning, a key domain of learning in which the goal is to learn a step-by-step pro-cedure. In contrast to previous approaches that require a large amount of domain-specific authoring, this framework takes as input only the procedure to be learned and produces scaffolding automatically. Directly encoding procedural knowledge in this way allows us to leverage techniques from the software engineering community. The framework uses test input generation tools to synthesize systematic progressions of practice problems that start easy, grow more difficult, adapt to the learner, and ultimately cover all of the important
Generating and Adapting Game Mechanics
"... Game designs often center on the game mechanics—rules governing the logical evolution of the game. We seek to de-velop an intelligent system that generates computer games and assists humans in designing games. As first steps to-wards this goal we present a composable and cross-domain representation ..."
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Game designs often center on the game mechanics—rules governing the logical evolution of the game. We seek to de-velop an intelligent system that generates computer games and assists humans in designing games. As first steps to-wards this goal we present a composable and cross-domain representation for game mechanics that draws from AI plan-ning action representations. We use a constraint solver to generate mechanics subject to design requirements on the form of those mechanics—what they do in the game. A planner takes a set of generated mechanics and tests whether those mechanics meet playability requirements—controlling how mechanics function in a game to affect player behav-ior. We demonstrate our system by modeling and generat-ing mechanics in a role-playing game, platformer game, and combined role-playing-platformer game. Categories and Subject Descriptors
