Experience-driven procedural content generation (extended abstract)

Procedural content generation is an increasingly important area of technology within modern human-computer interaction with direct applications in digital games, the semantic web, and interface, media and software design. The personalization of experience via the modeling of the user, coupled with the appropriate adjustment of the content according to user needs and preferences are important steps towards effective and meaningful content generation. This paper introduces a framework for procedural content generation driven by computational models of user experience we name Experience-Driven Procedural Content Generation. While the framework is generic and applicable to various subareas of human computer interaction, we employ games as an indicative example of content-intensive software that enables rich forms of interaction.The research was supported, in part, by the FP7 ICT projects C2Learn (318480) and iLearnRW (318803).peer-reviewe

Grounding truth via ordinal annotation

The question of how to best annotate affect within available content has been a milestone challenge for affective computing. Appropriate methods and tools addressing that question can provide better estimations of the ground truth which, in turn, may lead to more efficient affect detection and more reliable models of affect. This paper introduces a rank-based real-time annotation tool, we name AffectRank, and compares it against the popular rating-based real-time FeelTrace tool through a proofof- concept video annotation experiment. Results obtained suggest that the rank-based (ordinal) annotation approach proposed yields significantly higher inter-rater reliability and, thereby, approximation of the underlying ground truth. The key findings of the paper demonstrate that the current dominant practice in continuous affect annotation via rating-based labeling is detrimental to advancements in the field of affective computing.The authors would like to thank all annotators that participated in the reported experiments. We would also like to thank Gary Hili and Ryan Abela for providing access to the Eryi dataset. The work is supported, in part, by the EU-funded FP7 ICT iLearnRW project (project no: 318803).peer-reviewe

The platformer experience dataset

Player modeling and estimation of player experience have become very active research fields within affective computing, human computer interaction, and game artificial intelligence in recent years. For advancing our knowledge and understanding on player experience this paper introduces the Platformer Experience Dataset (PED) - the first open-access game experience corpus - that contains multiple modalities of user data of Super Mario Bros players. The open-access database aims to be used for player experience capture through context-based (i.e. game content), behavioral and visual recordings of platform game players. In addition, the database contains demographical data of the players and self-reported annotations of experience in two forms: ratings and ranks. PED opens up the way to desktop and console games that use video from webcameras and visual sensors and offer possibilities for holistic player experience modeling approaches that can, in turn, yield richer game personalization.peer-reviewe

To rank or to classify? Annotating stress for reliable PTSD profiling

In this paper we profile the stress responses of patients diagnosed with post-traumatic stress disorder (PTSD) to individual events in the game-based PTSD stress inoculation and exposure virtual environment StartleMart. Thirteen veterans suffering from PTSD play the game while we record their skin conductance. Game logs are used to identify individual events, and continuous decomposition analysis is applied to the skin conductance signals to derive event-related stress responses. The extracted skin conductance features from this analysis are used to profile each individual player in terms of stress response. We observe a large degree of variation across the 13 veterans which further validates the idiosyncratic nature of PTSD physiological manifestations. Further to game data and skin conductance signals we ask PTSD patients to indicate the most stressful event experienced (class-based annotation) and also compare the stress level of all events in a pairwise preference manner (rankbased annotation).We compare the two annotation stress schemes by correlating the self-reports to individual event-based stress manifestations. The self-reports collected through class-based annotation exhibit no correlation to physiological responses, whereas, the pairwise preferences yield significant correlations to all skin conductance features extracted via continuous decomposition analysis. The core findings of the paper suggest that reporting of stress preferences across events yields more reliable data that capture aspects of the stress experienced and that features extracted from skin conductance via continuous decomposition analysis offer appropriate predictors of stress manifestation across PTSD patients.This research was supported by the Danish Council for Technology and Innovation and by the EU funded FP7 ICT iLearnRW project (project no: 318803). We thank the PTSD patients who chose to support our research with their participation.peer-reviewe