Player behavioural modelling has grown from a means to improve the playing strength of computer programs that play classic games (e.g., chess), to a means for impacting the player experience and satisfaction in video games, as well as in cross-domain applications such as interactive storytelling. In this context, player behavioural modelling is concerned with two goals, namely (1) providing an interesting or effective game AI on the basis of player models and (2) creating a basis for game developers to personalise gameplay as a whole, and creating new user-driven game mechanics. In this article, we provide an overview of player behavioural modelling for video games by detailing four distinct approaches, namely (1) modelling player actions, (2) modelling player tactics, (3) modelling player strategies, and (4) player profiling. We conclude the article with an analysis on the applicability of the approaches for the domain of video games.
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This research is about the effect of story on player experience; it aimed to explore the difference between gameplay and story in a player’s experience and strived to measure it. The main problem for this research was one of knowledge; does story affect the player experience? Game creators often wonder if putting a story in a game is worth the time, money and effort. Does it affect the player experience in a positive way? Players wonder as well, if story even has a positive effect on player experience, putting gameplay and story up against each other. That is what this research is for, collecting information about the effect of story on player experience. The problem concerning the necessity of stories in games can be traced back to the long standing debate among the gaming community with two parties facing each other, one on the gameplay side and one on the story side. This also brings forth Clint Hocking’s problem, ludonarrative dissonance, the unharmonious state of a game’s gameplay and story, which, as he describes in his criticism piece Ludonarrative Dissonance in Bioshock (2007), can be experience breaking. Providing more information will hopefully make people think about the harmony that might exist between gameplay and story.
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This paper presents a method for generating player-driven narratives from visual inputs by exploring the visual analysis capabilities of multimodal large language models. By employing Bartle’s taxonomy of player types—Achievers, Explorers, Socializers, and Killers—our method creates stories that are tailored to different player characteristics. We conducted a fourfold experiment using a set of images extracted from a well-known game, generating distinct narratives for each player type that are aligned with the visual elements of the input images and specific player motivations. By adjusting narrative elements to emphasize achievement for Achievers, exploration for Explorers, social connections for Socializers, and competition for Killers, our system produced stories that adhere to established narratology principles while resonating with the characteristics of each player type. This approach can serve as a helping tool for game designers, offering new insights into how players might engage with game worlds through personalized image-driven narratives.
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New online stores and digital distribution methods have led to the development of alternative monetization models for video-games, such as free-to-play games with advertisements. Although there are many games using such models, until now the effect on the player experience from such interruptions has not been studied. In this controlled experiment, we requested that participants (N=236) play one of three different versions of a platformer game with: 1) no interruptions, 2) 30-second video advertisements, and 3) a multiple-choice questionnaire. We then evaluated the effects on the player experience. The study shows differences in their experiences, namely in: competence, immersion, annoyance, affects, and the reliability of the questionnaire answers. The contribution of this work is to identify which player experience variables are affected by interruptions, which can be valuable for selecting the business model and guiding the game design process.
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With the increased adoption of real-time objective measurements of player experience, advances have been made in characterising the dynamically changing aspects of the player experience during gameplay itself. A direct coupling to player action, however, is not without challenges. Many physiological responses, for instance, have an inherent delay, and often take some time to return to a baseline, providing challenges of interpretation when analysing rapidly changing gameplay on a micro level of interaction. The development of event-related, or phasic, measurements directly coupled to player actions provides additional insights, for instance through player modelling, but also through the use of behavioural characteristics of the human computer interaction itself. In this study, we focused on the latter, and measured keyboard pressure in a number of different, fast-paced action games. In this particular case, we related specific functional game actions (keyboard presses) to experiential player behaviour. We found keyboard pressure to be higher for avoidance as compared to approach-oriented actions. Additionally, the difference between avoidance and approach keyboard pressure related to levels of arousal. The findings illustrate the application potential of qualifying players’ functional actions at play (navigating in a game) and interpret player experience related to these actions through players’ real world behavioural characteristics like interface pressure.
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De Digitale Universiteit (DU) performed a quickscan to determine the usability of the IMS Question and TestInteroperability (QTI) specification as a format to store questions and tests developed for and by the consortium. The original report is available in Dutch from the website of De Digitale Universiteit and an unofficial English translation of that report can be downloaded. In October 2003, Canvas Learning Ltd., developers of the Canvas Canvas Learning Author and Canvas Learning Player responded to the Quickscan by sending their Canvas Flash player which could also render the test questions developed for the Quickscan. The Canvas Learning Player hadn't been tested as part of the original Quickscan because none of the partners within De Digitale Universiteit was using the application at that time. This addendum contains a short overview of the results of the tests for the Flash player as it was provided by Canvas Learning Ltd. All tests have been conducted by the author of the quickscan using the original test set. The set and the player used can be downloaded as a SCORM compliant package.
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The social dynamics of player communities in online games have been the subject of much research during the last decade. Following a systematic review of empirical research publications from 2000–2010, this article synthesizes the key methods and concepts researchers have used to study and characterize player communities. It also synthesizes the key aspects and operationalizations researchers have concentrated on. The analysis shows that qualitative approaches have been more common than quantitative ones. The concepts used to characterize player communities were often not clearly defined or overlapped in meaning. Yet they revealed a prevalence of micro (groups or teams), meso (guilds or organizations) and macro (communities and networks) perspectives. About 22 different aspects and operationalizations of player communities were identified. Six were most common, i.e. culture and social norms, social structuring, rationale, number of members, used information and communication technologies and time of existence. The article concludes with several suggestions for future research.
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Player Modeling is a research field that studies player characteristics by analyzing in-game behavior. We aim to develop independent models, which are transferable and useful beyond a game’s context. We shall demonstrate the feasibility of this approach by applying player models to crowdsourcing to predict workers’ task completion effectiveness. Specifically, we model a user’s Need for Cognition based on in-game behavior, and based on that try to assign appropriate tasks to workers.
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Numerous researchers have written about the social dynamics of player communities in multiplayer online games. Following a systematic review of refereed empirical research publications from 2000-2010, this article synthesizes the key methods and concepts researchers have used to study and characterize player communities, as well as the aspects and operationalizations they have concentrated on. The analysis shows that qualitative approaches have been more popular than quantitative. The concepts used to characterize player communities were often not clearly defined or overlapped in meaning. Yet they revealed a prevalence of micro (groups or teams), meso (guilds or organizations) and macro (communities and networks) perspectives. Eighteen different aspects and operationalizations of player communities were identified. Six of these were clearly most popular, i.e. social structuring, rationale, culture & social norms, used ICTs, number of members and time of existence. The article concludes with several perspectives and suggestions for future research.
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Numerous researchers have written about the social dynamics of player communities in multiplayer online games. Following a systematic review of refereed empirical research publications from 2000-2010, this article synthesizes the key methods and concepts researchers have used to study and characterize player communities, as well as the aspects and operationalizations they have concentrated on. The analysis shows that qualitative approaches have been more popular than quantitative. The concepts used to characterize player communities were often not clearly defined or overlapped in meaning. Yet they revealed a prevalence of micro (groups or teams), meso (guilds or organizations) and macro (communities and networks) perspectives. Eighteen different aspects and operationalizations of player communities were identified. Six of these were clearly most popular, i.e. social structuring, rationale, culture & social norms, used ICTs, number of members and time of existence. The article concludes with several perspectives and suggestions for future research.
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