Towards Generic Models of Player Experience

We report research on player modeling using psychophysiology and machine learning, conducted through interdisciplinary collaboration between researchers of computer science, psychology, and game design at Aalto University, Helsinki. First, we propose the Play Patterns And eXperience (PPAX) framework to connect three levels of game experience that previously had remained largely unconnected: game design patterns, the interplay of game context with player personality or tendencies, and state-of-the-art measures of experience (both subjective and non-subjective). Second, we describe our methodology for using machine learning to categorize game events to reveal corresponding patterns, culminating in an example experiment. We discuss the relation between automatically detected event clusters and game design patterns, and provide indications on how to incorporate personality profiles of players in the analysis. This novel interdisciplinary collaboration combines basic psychophysiology research with game design patterns and machine learning, and generates new knowledge about the interplay between game experience and design.

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.

Entertainment Computing - ICEC 2010, 9th International Conference, ICEC 2010, Seoul, Korea, September 8-11, 2010. Proceedings; 01/2010, 2010

Game designers empirically use psychological and sociological player’s model to create the gameplay of their video games. These models are generally implicit and always informal. A formal analysis of the player’s model leads to define efficient player behavior profile. It can have numerous applications, for instance adaptation of the content to the player’s ability and interest. Our work tries to find a rational way to assess Players Styles, concept suggested by Bartle [1] in 1996. The first step, state of the art of the player model, shows already some interesting criteria that can be used to classify player’s styles.

Proceedings of the ACM on Human-Computer Interaction, 2019

Different have been the attempts to use Procedural Content Generation via Machine Learning in game development. Among the others, some researchers have tried to adapt a game, or some part of it, to the user playing it. This approach has been called "adaptive game design". Contrarily to what it may seem, apparently the most interesting findings in this field have been made for drama managers, i.e. for the artificial intelligences that procedurally generate story flow. The paper takes the move from what seems to be a missing in current literature and it is aimed at proposing and discussing a possible procedural content generation via machine learning model that takes the latest approaches in machine learning applied to drama managers and combine them with findings from adaptive game design. The objective of the proposed model is to give players the best possible gaming experience of a highly branched game, depending on their attitudes towards the gaming world.

Experience assessment and design in the analysis of gameplay 2

2015

Analytical accounts chronicling engagement with digital games can always benefit from empirical data outlining the patterns of behavior produced by different players as they engage with the same game, or similar sequence within a game. This paper presents an extension to the novel method previously introduced in Marczak et al. (2013; 2015) and Schott et al. (2014), termed feedback-based game metrics, which exploits the audio and visual output of an activated game to produce accounts of player performance. This paper offers an account of an affiliated method, based on similarity matrix, which is derived from the same measurement process and that has yet been applied to the interests of game studies (over design oriented research) to determine the similarity or diversity within encounters with particular games. This paper introduces the method and illustrates its potential applications in the analysis of performance.

HAL (Le Centre pour la Communication Scientifique Directe), 2022

This paper describes an automated evaluation of the overall game experience using a synthetic agent, that we contextualize for First-Person Shooter games. This evaluation method is based on the characterization of the game experience through dynamics of major FPS games. We define dynamics as sequences of events that are meaningful for the player during the game session. As they trigger players' emotional responses, and influence their overall enjoyment and motivation, we classify them according to Motives for Play like curiosity, thrill-seeking, problem-solving, victory, and acquisition, in order to facilitate the evaluation process. Based on that, our evaluation method proposes to select synthetic agent routines that target a distinct game experience while playing a game session, using a selection of game dynamics. As the agent navigates through the level and interacts with opponents, dynamics may occur and, if so, are automatically identified, and then classified as Motives for Play. In the end, this classification can be used to evaluate the game experience and the quality of the level itself during playtesting sessions. It may also be utilized to assist the procedural generation of any level that target a specific game experience. CCS Concepts: • Human-centered computing → User models; • Applied computing → Computer games; Psychology.

Player preferences for different gaming styles or game elements has been a topic of interest in human-computer interaction for over a decade. However, current models suggested by the extant literature are generally based on classifying abstract gaming motivations or player archetypes. These concepts do not directly map onto the building blocks of games, taking away from the utility of the findings. To address this issue, we propose a conceptual framework of player preferences based on two dimensions: game elements and game playing styles. To investigate these two concepts, we conducted an exploratory empirical investigation of player preferences, which allowed us to create a taxonomy of nine groups of game elements and five groups of game playing styles. These two concepts are foundational to games, which means that our model can be used by designers to create games that are tailored to their target audience. In addition, we demonstrate that there are significant effects of gender and age on participants' preferences and discuss the implications of these findings.

2011

This paper presents a methodology which allows for an objective analysis of video game quality based on player behaviour. A mutually exclusive and exhaustive code of 5 behavioural states is presented based on an analysis of 10 users each playing 3 video games. The coding scheme is verified for inter-coder agreement with resulting Kappa values in the range of 0.74 to 0.91 (good agreement to very good agreement). Results of the game studies presented show that good games allow the player to enter the Engagement state more frequently, and keep them in that state for a longer duration than bad games. In particular, the results show that good games exhibit an overall net positive behaviour from the very early stages of gameplay. The paper concludes with suggestions for future work.

In this paper, we describe a methodology for capturing player experience while interacting with a game and we present a data-driven approach for modeling this interaction. We believe the best way to adapt games to a specific player is to use quantitative models of player experience derived from the in-game interaction. Therefore, we rely on crowd-sourced data collected about game context, players behavior and players self-reports of different affective states. Based on this information, we construct estimators of player experience using neuroevolutionary preference learning. We present the experimental setup and the results obtained from a recent case study where accurate estimators were constructed based on information collected from players playing a firstperson shooter game. The framework presented is part of a bigger picture where the generated models are utilized to tailor content generation to particular player's needs and playing characteristics.