This paper introduces a fully automatic method for generating video game tutorials. The AtDELFI system (AuTomatically DEsigning Legible, Full Instructions for games) was created to investigate procedural generation of instructions that teach players how to play video games. We present a representation of game rules and mechanics using a graph system as well as a tutorial generation method that uses said graph representation. We demonstrate the concept by testing it on games within the General Video Game Artificial Intelligence (GVG-AI) framework; the paper discusses tutorials generated for eight different games. Our findings suggest that a graph representation scheme works well for simple arcade style games such as Space Invaders and Pacman, but it appears that tutorials for more complex games might require higher-level understanding of the game than just single mechanics.

AtDelfi: Automatically Designing Legible, Full Instructions For Games

This paper introduces a fully automatic method for generating video game tutorials. The AtDELFI system (Automatically DEsigning Legible, Full Instructions for games) was created to investigate procedural generation of instructions that teach players how to play video games. We present a representation of game rules and mechanics using a graph system as well as a tutorial generation method that uses said graph representation. We demonstrate the concept by testing it on games within the General Video Game Artificial Intelligence (GVG-AI) framework; the paper discusses tutorials generated for eight different games. Our findings suggest that a graph representation scheme works well for simple arcade style games such as Space Invaders and Pacman, but it appears that tutorials for more complex games might require higher-level understanding of the game than just single mechanics.

AtDELFI: automatically designing legible, full instructions for games

Abstract Freemium open-world games have become one of the most profitable game genres through the sale of in-game items. However, despite the apparent commercial success of the freemium approach to open-world games, the antecedents of players’ in-game purchase intention have been understudied. In this study, we built a theoretical framework based on cognitive absorption (CA) theory to understand the mechanism underlying players’ in-game purchase intention in open-world games. Several hypotheses were proposed regarding how CA was formed and how CA’s influence on in-game purchase intention might be moderated by motivational affordances (i.e., autonomy, competence, and relatedness). These hypotheses were tested by partial least-squares structural equation modeling, using a sample of 490 valid responses among open-world game players. The results suggest that CA significantly increased players’ in-game purchase intention, which is strengthened by two motivational affordances (i.e., autonomy and competence). In addition, personal innovativeness, playfulness, and perceived affective quality are verified as important determinants of CA in open-world games. We discuss the related theoretical and practical implications of these findings.

Exploring Players’ in-Game Purchase Intention in Freemium Open-World Games: The Role of Cognitive Absorption and Motivational Affordances

The automatic generation of game tutorials is a challenging AI problem. While it is possible to generate annotations and instructions that explain to the player how the game is played, this paper focuses on generating a gameplay experience that introduces the player to a game mechanic. It evolves small levels for the Mario AI Framework that can only be beaten by an agent that knows how to perform specific actions in the game. It uses variations of a perfect A* agent that are limited in various ways, such as not being able to jump high or see enemies, to test how failing to do certain actions can stop the player from beating the level.

Generating Levels That Teach Mechanics

The article proposes and discusses the term of ‘ludotopian dissonance’ in reference to flawed open-world design in computer role-playing games (cRPGs). Much like ludonarrative dissonance, this concept shall address a paradox of narrative credibility ‐ this time, however,
 narrowed to the gameworld itself rather than to gameplay or storyline. This case study of Cyberpunk 2077’s world-building is supplemented with reflection upon the idea of openness or ‘openworldlness’ (‘what makes a given imaginary world truly open? Is it possible?
 Or is it viable for video games?’), as well as with research on explorable imaginary worlds (predominantly from the angle of transmedial narratology and interdisciplinary world-building studies). The aim of the article is to reiterate the necessity of design worlds that do not only serve
 as a container for storytelling, but also provide the players with an inhabitable, performative ludotopia which does not produce any dissonances between the credible storyworld and incredulous gameworld.

On the pseudo-open world and ludotopian dissonance: A curious case of Cyberpunk 2077

Open world games present players with more freedom than games with linear progression structures. However, without clearly-defined objectives, they often leave players without a sense of purpose. Most of the time, quests and objectives are hand-authored and overlaid atop an open world's mechanics. But what if they could be generated organically from the gameplay itself? The goal of our project was to develop a model of the mechanics in Minecraft that could be used to determine the ideal placement of objectives in an open world setting. We formalized the game logic of Minecraft in terms of logical rules that can be manipulated in two ways: they may be executed to generate graphs representative of the player experience when playing an open world game with little developer direction; and they may be statically analyzed to determine dependency orderings, feedback loops, and bottlenecks. These analyses may then be used to place achievements on gameplay actions algorithmically.

Deriving quests from open world mechanics

Deriving Quests from Open World Mechanics

Expert-crafted puzzle games are notable for their ability to wordlessly teach players deep and interesting consequences of mechanics. Cognitive science theories suggest that this learning happens through an iterative process of hypothesizing, failure, and revision of mental models. In this work, we pose the question of what it would take to generate puzzle game levels and progressions that best support player satisfaction and systematic understanding of mechanics by integrating an understanding of player mental models. We present a preliminary investigation consisting of a new puzzle game Laserverse, whose mechanics are designed to interact with each other in a wide array of combinations, a set of handauthored levels that combine these mechanics hierarchically, and an algorithm for organizing the levels into progressions to test hypotheses based on a theory of mental model formation. This work sets the stage for empirical validation of puzzle game design principles, which in turn inform a new approach to player modeling based on mental model matching theory.

/pdf/generating-puzzle-progressions-to-study-mental-model-1kgu1z893f.pdf

Generating Puzzle Progressions to Study Mental Model Matching.

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Deriving quests from open world mechanics

Deriving Quests from Open World Mechanics

Generating Puzzle Progressions to Study Mental Model Matching.