5,967 research outputs found
Generic physiological features as predictors of player experience
This paper examines the generality of features extracted from heart rate (HR) and skin conductance (SC) signals as predictors of self-reported player affect expressed as pairwise preferences. Artificial neural networks are trained to accurately map physiological features to expressed affect in two dissimilar and independent game surveys. The performance of the obtained affective models which are trained on one game is tested on the unseen physiological and self-reported data of the other game. Results in this early study suggest that there exist features of HR and SC such as average HR and one and two-step SC variation that are able to predict affective states across games of different genre and dissimilar game mechanics.peer-reviewe
Towards general models of player affect
While the primary focus of affective computing has
been on constructing efficient and reliable models of affect,
the vast majority of such models are limited to a specific task
and domain. This paper, instead, investigates how computational
models of affect can be general across dissimilar tasks; in
particular, in modeling the experience of playing very different
video games. We use three dissimilar games whose players
annotated their arousal levels on video recordings of their own
playthroughs. We construct models mapping ranks of arousal to
skin conductance and gameplay logs via preference learning and
we use a form of cross-game validation to test the generality of the
obtained models on unseen games. Our initial results comparing
between absolute and relative measures of the arousal annotation
values indicate that we can obtain more general models of player
affect if we process the model output in an ordinal fashion.peer-reviewe
Establishing consensus of position-specific predictors for elite youth soccer in England
Purpose: To construct a valid and reliable methodology for the development of position-specific predictors deemed appropriate for talent identification purposes within elite youth soccer in England.
Method: N = 10 panel experts participated in a three-step modified e-Delphi poll to generate consensus on a series of generic youth player attributes. A follow-up electronic survey completed by coaches, scouts and recruitment staff (n = 99) ranked these attributes to specific player-positions.
Results: A final list of 44 player attributes found consensus using the three-step modified e-Delphi poll. Findings indicated that player-positional attributes considered most important in the youth phase are more psychological and technical than physiological or anthropometric. Despite âhiddenâ attributes (e.g., coachability, flair, versatility, and vision) finding consensus on the e-Delphi poll, there was no evidence to support these traits when associated with a specific playing position.
Conclusion: For those practitioners responsible for talent recruitment, our findings may provide greater understanding of the multiple attributes required for some playing positions. However, further ecological research is required to assess the veracity of our claims
A taxonomy and state of the art revision on affective games
Affective Games are a sub-field of Affective Computing that tries to study how
to design videogames that are able to react to the emotions expressed by the
player, as well as provoking desired emotions to them. To achieve those goals
it is necessary to research on how to measure and detect human emotions using
a computer, and how to adapt videogames to the perceived emotions to finally
provoke them to the players. This work presents a taxonomy for research on
affective games centring on the aforementioned issues. Here we devise as well a
revision of the most relevant published works known to the authors on this area.
Finally, we analyse and discuss which important research problem are yet open
and might be tackled by future investigations in the area of Affective GamesThis work has been co-funded by the following research projects: EphemeCH (TIN2014-56494-C4-{1,4}-P) and DeepBio (TIN2017-85727-C4-3-P) by Spanish Ministry of Economy and Competitivity, under the European Regional Development Fund FEDER, and Justice Programme of the European Union (2014â2020) 723180 â RiskTrack â JUST-2015-JCOO-AG/JUST-2015-JCOO-AG-
General general game AI
Arguably the grand goal of artificial intelligence
research is to produce machines with general intelligence: the
capacity to solve multiple problems, not just one. Artificial
intelligence (AI) has investigated the general intelligence capacity
of machines within the domain of games more than any other
domain given the ideal properties of games for that purpose:
controlled yet interesting and computationally hard problems.
This line of research, however, has so far focused solely on
one specific way of which intelligence can be applied to games:
playing them. In this paper, we build on the general game-playing
paradigm and expand it to cater for all core AI tasks within a
game design process. That includes general player experience
and behavior modeling, general non-player character behavior,
general AI-assisted tools, general level generation and complete
game generation. The new scope for general general game AI
beyond game-playing broadens the applicability and capacity of
AI algorithms and our understanding of intelligence as tested
in a creative domain that interweaves problem solving, art, and
engineering.peer-reviewe
Mining multimodal sequential patterns : a case study on affect detection
Temporal data from multimodal interaction such as speech and bio-signals cannot be easily analysed without a preprocessing phase through which some key characteristics of the signals are extracted. Typically, standard statistical signal features such as average values are calculated prior to the analysis and, subsequently, are presented either to a multimodal fusion mechanism or a computational model of the interaction. This paper proposes a feature extraction methodology which is based on frequent sequence mining within and across multiple modalities of user input. The proposed method is applied for the fusion of physiological signals and gameplay information in a game survey dataset. The obtained sequences are analysed and used as predictors of user affect resulting in computational models of equal or higher accuracy compared to the models built on standard statistical features.peer-reviewe
Establishing consensus of position-specific predictors for elite youth soccer in England
Purpose: To construct a valid and reliable methodology for the development of position- specific predictors deemed appropriate for talent identification purposes within elite youth soccer in England. Method: N = 10 experts participated in a three-step modified e-Delphi poll to generate consensus on a series of generic youth player attributes. A follow up electronic survey completed by coaches, scouts and recruitment staff (n = 99) ranked these attributes to specific player- positions. Results: A final list of 44 player attributes found consensus using the three-step modified e-Delphi poll and the findings indicated that player-positional attributes considered most important at the youth phase are more psychological and technical than physiological or anthropometric. Despite âhiddenâ attributes (e.g. coachability, flair, versatility, vision etc) finding consensus on the e-Delphi poll, there was no evidence to support these traits when associated with a specific playing position. Conclusion: For those practitioners responsible for talent recruitment, our findings may provide greater understanding of the multiple attributes required for some playing positions. However, new ecological research is required to assess the veracity of our claims
Your Gameplay Says It All: Modelling Motivation in Tom Clancy's The Division
Is it possible to predict the motivation of players just by observing their
gameplay data? Even if so, how should we measure motivation in the first place?
To address the above questions, on the one end, we collect a large dataset of
gameplay data from players of the popular game Tom Clancy's The Division. On
the other end, we ask them to report their levels of competence, autonomy,
relatedness and presence using the Ubisoft Perceived Experience Questionnaire.
After processing the survey responses in an ordinal fashion we employ
preference learning methods based on support vector machines to infer the
mapping between gameplay and the reported four motivation factors. Our key
findings suggest that gameplay features are strong predictors of player
motivation as the best obtained models reach accuracies of near certainty, from
92% up to 94% on unseen players.Comment: Version accepted for IEEE Conference on Games, 201
Stress detection for PTSD via the StartleMart game
Computer games have recently shown promise as
a diagnostic and treatment tool for psychiatric rehabilitation.
This paper examines the positive impact of affect detection and
advanced game technology on the treatment of mental diagnoses
such as Post Traumatic Stress Disorder (PTSD). For that purpose,
we couple game design and game technology with stress detection
for the automatic profiling and the personalized treatment of
PTSD via game-based exposure therapy and stress inoculation
training. The PTSD treatment game we designed forces the
player to go through various stressful experiences while a stress
detection mechanism profiles the severity and type of PTSD
via skin conductance responses to those in-game stress elicitors.
The initial study and analysis of 14 PTSD-diagnosed veteran
soldiers presented in this paper reveals clear correspondence
between diagnostic standard measures of PTSD severity and skin
conductance responses. Significant correlations between physiological
responses and subjective evaluations of the stressfulness of
experiences, represented as pairwise preferences, are also found.
We conclude that this supports the use of the simulation as a
relevant treatment tool for stress inoculation training. This points
to future avenues of research toward discerning between degrees
and types of PTSD using game-based diagnostic and treatment
tools.This research was supported by the Danish Council for
Technology and Innovation under the Games for Health project
and by the FP7 ICT project SIREN (project no: 258453).peer-reviewe
Don't classify ratings of affect ; rank them!
How should affect be appropriately annotated and how should machine learning best be employed to map
manifestations of affect to affect annotations? What is the use of ratings of affect for the study of affective computing and
how should we treat them? These are the key questions this paper attempts to address by investigating the impact of dissimilar
representations of annotated affect on the efficacy of affect modelling. In particular, we compare several different binary-class
and pairwise preference representations for automatically learning from ratings of affect. The representations are compared and
tested on three datasets: one synthetic dataset (testing âin vitroâ) and two affective datasets (testing âin vivoâ). The synthetic
dataset couples a number of attributes with generated rating values. The two affective datasets contain physiological and
contextual user attributes, and speech attributes, respectively; these attributes are coupled with ratings of various affective
and cognitive states. The main results of the paper suggest that ratings (when used) should be naturally transformed to ordinal
(ranked) representations for obtaining more reliable and generalisable models of affect. The findings of this paper have a direct
impact on affect annotation and modelling research but, most importantly, challenge the traditional state-of-practice in affective
computing and psychometrics at large.peer-reviewe
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