Predicting Player Experience without the Player

With computers becoming ubiquitous and high resolution graphics reaching the next level, computer games have become a major source of entertainment. It has been a tedious task for game developers to measure the entertainment value of the computer games. The entertainment value of a game does depend upon the genre of the game in addition to the game contents. In this paper, we propose a set of entertainment metrics for the platform genre of games. The set of entertainment metrics is proposed based upon certain theories on entertainment in computer games. To test the metrics, we use an evolutionary algorithm for automated generation of game rules which are entertaining. The proposed approach starts with an initial set of randomly generated games and, based upon the proposed metrics as an objective function, guides the evolutionary process. The results produced are counterchecked against the entertainment criteria of humans by conducting a human user survey and a controller learning ability experiment. The proposed metrics and the evolutionary process of generating games can be employed by any platform game for the purpose of automatic generation of interesting games provided an initial search space is given.

Proceedings of Artificial …, 2010

In this paper, we show that personalized levels can be automatically generated for platform games. We build on previous work, where models were derived that predicted player experience based on features of level design and on playing styles. These models are constructed using preference learning, based on questionnaires administered to players after playing different levels. The contributions of the current paper are (1) more accurate models based on a much larger data set; (2) a mechanism for adapting level design parameters to given players and playing style; (3) evaluation of this adaptation mechanism using both algorithmic and human players.

Lecture Notes in Computer Science, 2013

In this article we bring the concepts of narrativism and ludology to automatic level generation for platform videogames. The initial motivation is to understand how this genre has been used as a storytelling medium. Based on a narrative theory of games, the differences among several titles have been identified. In addition, we propose a set of abstraction layers to describe the content of a quest-based story in the particular context of videogames. Regarding automatic level generation for platform videogames, we observed that the existing approaches are directed to lower abstraction concepts such as avatar movements without a particular context or meaning. This leads us to the challenge of automatically creating more contextualized levels rather than only a set of consistent and playable entertaining tasks. With that in mind, a set of higher level design patterns are presented and their potential usages are envisioned and discussed.

2011

With the current set of design tools and methods available to game designers, vast portions of the space of possible games are not currently reachable. In the past, technological advances such as improved graphics and new controllers have driven the creation of new forms of gameplay, but games have still not made great strides into new gameplay experiences. We argue that the development of innovative artificial intelligence (AI) systems plays a crucial role in the exploration of currently unreachable spaces. To aid in exploration, we suggest a practice called AI-based game design, an iterative design process that deeply integrates the affordances of an AI system within the context of game design. We have applied this process in our own projects, and in this paper we present how it has pushed the boundaries of current game genres and experiences, as well as discuss the future AI-based game design.

International Journal of Human-Computer Studies, 2020

Game development often requires a multidisciplinary team, demands substantial time and budget, and leads to a limited number of game contents (e.g., levels). Procedural Content Generation (PCG) can remedy some of these problems, aiding with the automatic creation of content such as levels and graphics, in both the development and playing time. However, little research has been performed in terms of how PCG influences players, especially on Digital Math Games (DMG). This article addresses this problem by investigating the interactions of players with a DMG that uses PCG, investigating the hypothesis that interacting with this intervention can provide experiences as good as human-designed content. To accomplish this goal, an A/B test was performed wherein the only difference was that one version (static, N = 242) had human-designed levels, whereas the other (dynamic, N = 265) provided procedurally generated levels. To validate the approach, a two-sample experiment was designed in which each sample played a single version and, thereafter, self-reported their experiences through questionnaires. We contribute by showing how the participants interactions with a DMG are reported in terms of (1) fun, (2) willingness to play the game again, and (3) curiosity, in addition to how they (4) describe their experiences. Our findings show that samples' experiences did not significantly differ on the four metrics, but did differ on in-game performance. We discuss possible factors that might have influenced players' experiences, in terms of the participants performances and their demographic attributes, and how our findings contribute to human interaction with computers.

2018

textabstractProcedural level generation for games is an active field of research with successful applications. However, how to generate content that embodies design intent is still an open research question. Level designers lack abstractions and tools for authoring generated artifacts for affecting emotion. We propose a novel pattern language for generative level design inspired by Appraisal Theory. Its patterns enable designers to add meaning, depth, and cohesiveness to the resulting content, and modify artifacts to make the content more engaging. We illustrate how these patterns can be implemented in a generative grammar for level generation for an adventure game. Formative evaluation of generated level content demonstrates the feasibility of the approach and suggests points for further improvements. Future work could focus on other elements which seem important for affecting emotions, including pacing, perception, and expectation.

2018

In this paper, we explore the impact of the most common building blocks of game AI systems on player experience. We conduct an extensive survey of the most common techniques applied in practice, and then analyze their individual and combined impact on player experience. Next, we argue that a systemic AI that guides the player adversary on a macro level, raises the replay value that keeps the users engaged and enhances the overall player experience. We observe player behavior through a series of playtesting sessions on an AI-based game prototype, with variations on each AI subsystem. Finally, we provide an analysis of our results.

2007

The current state-of-the-art in intelligent game design using Artificial Intelligence (AI) techniques is mainly focused on generating human-like and intelligent characters. Even though complex opponent behaviors emerge through various machine learning techniques, there is generally no further analysis of whether these behaviors contribute to the satisfaction of the player. The implicit hypothesis motivating this research is that intelligent opponent behaviors enable the player to gain more satisfaction from the game. This hypothesis may well be true; however, since no notion of entertainment or enjoyment is explicitly defined, there is therefore no evidence that a specific opponent behavior generates enjoyable games.

Proceedings of the Genetic and Evolutionary Computation Conference 2016, 2016

This paper presents a framework and an initial study in general video game level generation, the problem of generating levels for not only a single game but for any game within a specified domain. While existing level generators are tailored to a particular game, this new challenge requires generators to take into account the constraints and affordances of games that might not even have been designed when the generator was constructed. The framework presented here builds on the General Video Game AI framework (GVG-AI) and the Video Game Description Language (VGDL), in order to reap synergies from research activities connected to the General Video Game Playing Competition. The framework will also form the basis for a new track of this competition. In addition to the framework, the paper presents three general level generators and an empirical comparison of their qualities.

2014

In this chapter, we explore methods for automatically generating game content — and games themselves — adapted to individual players, in order to improve their playing experience or achieve a desired effect. This goes beyond notions of mere replayability, and involves modelling player needs to maximise their enjoyment, involvement and interest in the game being played. We identify three main aspects of this process: Generation of new content and rule sets; Measurement of this content and the player; Adaptation of the game to change player experience. This process forms a feedback loop of constant refinement, as games are continually improved while being played. Framed within this methodology, we present an overview of our recent and ongoing research in this area. This is illustrated by a number of case studies that demonstrate these ideas in action over a variety of game types, including: 3D action games, arcade games, platformers, board games, puzzles and open world games. We draw ...