Mining Rules from Player Experience and Activity Data

The power of using machine learning to improve or investigate the experience of play is only beginning to be realised. For instance, the experience of play is a psychological phenomenon, yet common psychological concepts such as the typology of temperaments have not been widely utilised in game design or research. An effective player typology provides a model by which we can analyse player behaviour. We present a real-time classifier of player type, implemented in the test-bed game Pac-Man. Decision Tree algorithms CART and C5.0 were trained on labels from the DGD player typology (Bateman and Boon, 21st century game design, vol. 1, 2005). The classifier is then built by selecting rules from the Decision Trees using a ruleperformance metric, and experimentally validated. We achieve ∼70 % accuracy in this validation testing. We further analyse the concept descriptions learned by the Decision Trees. The algorithm output is examined with respect to a set of hypotheses on player behaviour. A set of open questions is then posed against the test data obtained from validation testing, to illustrate the further insights possible from extended analysis.

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.

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.

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.

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2016

The success of electronic sports (eSports), where professional gamers participate in competitive leagues and tournaments, brings new challenges for the video game industry. Other than fun, games must be difficult and challenging for eSports professionals but still easy and enjoyable for amateurs. In this article, we consider Multi-player Online Battle Arena games (MOBA) and particularly, "Defense of the Ancients 2", commonly known simply as DOTA2. In this context, a challenge is to propose data analysis methods and metrics that help players to improve their skills. We design a data mining-based method that discovers strategic patterns from historical behavioral traces: Given a model encoding an expected way of playing (the norm), we are interested in patterns deviating from the norm that may explain a game outcome from which player can learn more efficient ways of playing. The method is formally introduced and shown to be adaptable to different scenarios. Finally, we provide an experimental evaluation over a dataset of 10, 000 behavioral game traces.

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.

2020 IEEE Conference on Games (CoG), 2020

Competitive gaming, or esports, is now wellestablished and brought the game industry in a novel era. It comes with many challenges among which evaluating the level of a player, given the strategies and skills she masters. We are interested in automatically identifying the so called skillshots from game traces of Rocket League, a "soccer with rocketpowered cars" game. From a pure data point of view, each skill execution is unique and standard pattern matching may be insufficient. We propose a non trivial data-centric approach based on pattern mining and supervised learning techniques. We show through an extensive set of experiments that most of Rocket League skillshots can be efficiently detected and used for player modelling. It unveils applications for match making, supporting game commentators and learning systems among others.

2009

User-oriented research in the game industry is undergoing a change from relying on informal user-testing methods adapted directly from productivity software development to integrating modern approaches to usability-and user experience testing. Gameplay metrics analysis form one of these techniques, being based on instrumentation methods in HCI. Gameplay metrics are instrumentation data about the user behavior and user-game interaction, and can be collected during testing, production and the live period of the lifetime of a digital game. The use of instrumentation data is relatively new to commercial game development, and remains a relatively unexplored method of user research. In this paper, the focus is on utilizing game metrics for informing the analysis of gameplay during commercial game production as well as in research contexts. A series of case studies are presented, focusing on the major commercial game titles Kane & Lynch and Fragile Alliance.

2011

Observation and analysis of a phenomenon at unprecedented levels of granularity not only furthers our understanding of it, but also transforms the way it is studied. For instance, invention of gene-sequencing and computational analysis transformed the life sciences, creating fields of inquiry such as genomics, proteomics, etc., and the Hubble Space Telescope has furthered the ability of humanity to look much farther beyond what we could otherwise.

International Conference on the Foundations of Digital Games, 2020

Many successful games rely heavily on data analytics to understand players and inform design. Popular methodologies focus on machine learning and statistical analysis of aggregated data. While effective in extracting information regarding player action, much of the context regarding when and how those actions occurred is lost. Qualitative methods allow researchers to examine context and derive meaningful explanations about the goals and motivations behind player behavior, but are difficult to scale. In this paper, we build on previous work by combining two existing methodologies: Interactive Behavior Analytics (IBA) [2] and sequence analysis (SA), in order to create a novel, mixed methods, human-in-the-loop data analysis methodology that uses behavioral labels and visualizations to allow analysts to examine player behavior in a way that is context sensitive, scalable, and generalizable. We present the methodology along with a case study demonstrating how it can be used to analyze behavioral patterns of teamwork in the popular multiplayer game Defense of the Ancients 2 (DotA 2). CCS CONCEPTS • Human-centered computing → Visualization.