Behavlets: a method for practical player modelling using psychology-based player traits and domain specific features

Abstract

As player demographics broaden it has become important to understand variation in player types. Improved player models can help game designers create games that accommodate a range of playing styles, and may also facilitate the design of systems that detect the currently-expressed player type and adapt dynamically in real-time. Existing approaches can model players, but most focus on tracking and classifying behaviour based on simple functional metrics such as deaths, specific choices, player avatar attributes, and completion times. We describe a novel approach which seeks to leverage expert domain knowledge using a theoretical framework linking behaviour and game design patterns. The aim is to derive features of play from sequences of actions which are intrinsically informative about behaviour-which, because they are directly interpretable with respect to psychological theory of behaviour, we name 'Behavlets'. We present the theoretical underpinning of this approach from research areas including psychology, temperament theory, player modelling, and game composition. The Behavlet creation process is described in detail; illustrated using a clone of the well-known game Pac-Man, with data gathered from 100 participants. A workshop-based evaluation study is also presented, where nine game design expert participants were briefed on the Behavlet concepts and requisite models, Electronic supplementary material The online version of this article (

Developing Emotion-Aware, Advanced Learning Technologies: A Taxonomy of Approaches and Features

“…Next, proactive user-adaptive approaches can be reviewed and individual differences that stand to be important for the type of ALT or content (e.g., gender for math) can be strategically prioritized in the development of the academic environment and its features. Proactive, user-adaptive approaches rely, however, on data related to individual differences being collected and analyzed/ categorized, typically in self-report form, unless analytics for inferring traits (like personality) from in-system behaviors are available (Cowley and Charles 2016). Finally, decisions about reactive approaches must also be made.…”

Preface to the special issue on personality in personalized systems

“…The papers in this special issue (Fernández-Tobías et al 2016;Cowley and Charles 2016;Harley et al 2016;Lepri et al 2016) illustrate some of the approaches that can be taken to design personality-based user models and adaptive systems.…”

“…This special issue contains articles that describe the state of the art in (i) automatic and unobtrusive personality acquisition from social media (Farnadi et al 2016) and mobile phone data (Lepri et al 2016) (ii) personality-based user models in behavioral change applications (Lepri et al 2016), intelligent tutoring systems (Harley et al 2016) and games (Cowley and Charles 2016), and (iii) usage of personality in recommender systems (Fernández-Tobías et al 2016).…”

“…When only one domain is available, active learning should be used. Cowley and Charles (2016) deals with personality in computer games players modeling. The authors introduce the term behavlets for describing a set of features that describe the behavior of game players based on the temperament theory of personality.…”

Automated Playtesting With Procedural Personas Through MCTS With Evolved Heuristics

“…Perhaps the most obvious approach is to use some form of supervised learning to derive a model from play traces [18], [19]. Cowley et al developed the concept of behavlets, features of play derived from observed action sequences, structured through a topdown application of psychological temperament theory combined with machine learning [20]. While behavlets can be used as generative models, they do not allow for the specification of player motivations without observations.…”