8 research outputs found
Comparative fun analysis in the innovative playware game platform
This paper presents comparative fun experiments in the
innovative playground ‘Playware’ which is a
combination of physical and virtual com ponents for
activating physical and social children’s play. For this
purpose, a quantitative approach to entertainment
modeling based on psychological studies in the field of
computer games is introduced. The paper investigates
quantitatively how the qualitative factors of challenge,
curiosity and fantasy contribute to children’s
entertainment when playing Playware games. Statistical
analysis of children’s self-reports shows that objectively
children’s notion of entertainment correlates highly with
the fantasy factor whereas desired levels of challenge
and curiosity depend on the individual child’s
requirements.peer-reviewe
Capturing entertainment through heart rate dynamics in the playware playground
This paper introduces a statistical approach for capturing
entertainment in real-time through physiological signals within interactive
playgrounds inspired by computer games. For this purpose children’s heart rate
(HR) signals and judgement on entertainment are obtained from experiments on
the innovative Playware playground. A comprehensive statistical analysis
shows that children’s notion of entertainment correlates highly with their
average HR during the game.peer-reviewe
Promoting children’s physical activity using adaptive playgrounds
This abstract introduces the innovative Playware playground and how it can be utilized for
promoting children’s physical activity and thus partly addressing issues related to increasing obesity
problems in the western society. Playware allows for the use of intelligent technology to create the kind
of leisure activity normally labeled play, i.e. intelligent hardware and software which aims at producing play
and playful experiences amongst users. Playware with ambient intelligence characteristics can be
personalized, adaptive and anticipatory: it can be integrated into real physical environments (i.e.
playgrounds) so that users can freely and interactively utilize it allowing emergence of creative and active
plays.
Experiments within the Playware playground have demonstrated a significant correlation between the
level of children’s perceived entertainment (fun) and the average response time that children interact with the
playground. The obtained effect appears to be consistent with theoretical approaches on the interplay
between response time and the engagement level within human computer interactive systems. Moreover,
preliminary studies on physiological signals of children playing with Playware games have already shown
the significant effect of average heart rate (HR) to children’s entertainment. Thus the hypothesis drawn
here is that the higher the average response time of children during a game the higher the entertainment value
of the game and furthermore the higher their physical activity through their average HR.
The Playware playground has been augmented with an intelligent adaptation mechanism, which
efficiently recognizes an individual child’s playing behavior and adapts the playground game according to
the child’s individual desires. Several experiments have been conducted using adaptation mechanisms
designed in order to increase children’s physical activity. It has been shown, that individual play
characteristics, such as the total number of interactions with the playground and the average response time of
the interactions increase significantly with the use of the adaptation mechanism, providing evidence for the
mechanism’s appropriateness to effectively augment the game’s entertainment value and promote children’s
physical activity.peer-reviewe
Preliminary studies for capturing entertainment through physiology in physical play
This report presents preliminary physical control experiments for capturing and modeling the affective state of entertainment — that is, whether people are having "fun" — of users of the innovative Play-ware playground, an interactive physical playground. The goal is to con-struct, using representative statistics computed from children's physio-logical hear rate (HR) signals, an estimator of the degree to which games provided by the playground engage the players. For this purpose chil-dren's HR signals, and their expressed preferences of how much "fun" particular game variants are, are obtained from experiments using games implemented on the Playware playground. Neuro-evolution techniques combined with feature set selection methods permit the construction of user models that predict reported entertainment preferences given HR features. These models are expressed as artificial neural networks and are demonstrated and evaluated on two Playware games and the pre-liminary control task requiring physical activity. Results demonstrate that the proposed preliminary control experiment is not an appropriate control for physical activity effects since it may generate HR dynamics rather easy to separate from game-play HR dynamics, and allows one to distinguish entertaining game-play from exercise purely on the artificial basis of the kind of physical activity taking place. Conclusions derived from this study constitute the basis for the design of more appropriate control experiments and user models in future studies.peer-reviewe
Human-centred design methods : developing scenarios for robot assisted play informed by user panels and field trials
Original article can be found at: http://www.sciencedirect.com/ Copyright ElsevierThis article describes the user-centred development of play scenarios for robot assisted play, as part of the multidisciplinary IROMEC1 project that develops a novel robotic toy for children with special needs. The project investigates how robotic toys can become social mediators, encouraging children with special needs to discover a range of play styles, from solitary to collaborative play (with peers, carers/teachers, parents, etc.). This article explains the developmental process of constructing relevant play scenarios for children with different special needs. Results are presented from consultation with panel of experts (therapists, teachers, parents) who advised on the play needs for the various target user groups and who helped investigate how robotic toys could be used as a play tool to assist in the children’s development. Examples from experimental investigations are provided which have informed the development of scenarios throughout the design process. We conclude by pointing out the potential benefit of this work to a variety of research projects and applications involving human–robot interactions.Peer reviewe
Proceedings of the SAB'06 Workshop on Adaptive Approaches for Optimizing Player Satisfaction in Computer and Physical Games
These proceedings contain the papers presented at the Workshop on Adaptive approaches
for Optimizing Player Satisfaction in Computer and Physical Games held at the Ninth
international conference on the Simulation of Adaptive Behavior (SAB’06): From
Animals to Animats 9 in Rome, Italy on 1 October 2006.
We were motivated by the current state-of-the-art in intelligent game design using
adaptive approaches. Artificial Intelligence (AI) techniques are mainly focused on
generating human-like and intelligent character behaviors. Meanwhile there is generally
little further analysis of whether these behaviors contribute to the satisfaction of the
player. The implicit hypothesis motivating this research is that intelligent opponent
behaviors enable the player to gain more satisfaction from the game. This hypothesis may
well be true; however, since no notion of entertainment or enjoyment is explicitly
defined, there is therefore little evidence that a specific character behavior generates
enjoyable games.
Our objective for holding this workshop was to encourage the study, development,
integration, and evaluation of adaptive methodologies based on richer forms of humanmachine
interaction for augmenting gameplay experiences for the player. We wanted to
encourage a dialogue among researchers in AI, human-computer interaction and
psychology disciplines who investigate dissimilar methodologies for improving gameplay
experiences. We expected that this workshop would yield an understanding of state-ofthe-
art approaches for capturing and augmenting player satisfaction in interactive systems
such as computer games.
Our invited speaker was Hakon Steinø, Technical Producer of IO-Interactive, who
discussed applied AI research at IO-Interactive, portrayed the future trends of AI in
computer game industry and debated the use of academic-oriented methodologies for
augmenting player satisfaction. The sessions of presentations and discussions where
classified into three themes: Adaptive Learning, Examples of Adaptive Games and Player
Modeling.
The Workshop Committee did a great job in providing suggestions and informative
reviews for the submissions; thank you! This workshop was in part supported by the
Danish National Research Council (project no: 274-05-0511). Finally, thanks to all the
participants; we hope you found this to be useful!peer-reviewe
An Ordinal Approach to Affective Computing
Both depression prediction and emotion recognition systems are often based on ordinal ground truth due to subjectively annotated datasets. Yet, both have so far been posed as classification or regression problems. These naive approaches have fundamental issues because they are not focused on ordering, unlike ordinal regression, which is the most appropriate for truly ordinal ground truth. Ordinal regression to date offers comparatively fewer, more limited methods when compared with other branches in machine learning, and its usage has been limited to specific research domains. Accordingly, this thesis presents investigations into ordinal approaches for affective computing by describing a consistent framework to understand all ordinal system designs, proposing ordinal systems for large datasets, and introducing tools and principles to select suitable system designs and evaluation methods.
First, three learning approaches are compared using the support vector framework to establish the empirical advantages of ordinal regression, which is lacking from the current literature. Results on depression and emotion corpora indicate that ordinal regression with proper tuning can improve existing depression and emotion systems. Ordinal logistic regression (OLR), which is an extension of logistic regression for ordinal scales, contributes to a number of model structures, from which the best structure must be chosen. Exploiting the newly proposed computationally efficient greedy algorithm for model structure selection (GREP), OLR outperformed or was comparable with state-of-the-art depression systems on two benchmark depression speech datasets.
Deep learning has dominated many affective computing fields, and hence ordinal deep learning is an attractive prospect. However, it is under-studied even in the machine learning literature, which motivates an in-depth analysis of appropriate network architectures and loss functions. One of the significant outcomes of this analysis is the introduction of RankCNet, a novel ordinal network which utilises a surrogate loss function of rank correlation.
Not only the modelling algorithm but the choice of evaluation measure depends on the nature of the ground truth. Rank correlation measures, which are sensitive to ordering, are more apt for ordinal problems than common classification or regression measures that ignore ordering information. Although rank-based evaluation for ordinal problems is not new, so far in affective computing, ordinality of the ground truth has been widely ignored during evaluation. Hence, a systematic analysis in the affective computing context is presented, to provide clarity and encourage careful choice of evaluation measures. Another contribution is a neural network framework with a novel multi-term loss function to assess the ordinality of ordinally-annotated datasets, which can guide the selection of suitable learning and evaluation methods. Experiments on multiple synthetic and affective speech datasets reveal that the proposed system can offer reliable and meaningful predictions about the ordinality of a given dataset.
Overall, the novel contributions and findings presented in this thesis not only improve prediction accuracy but also encourage future research towards ordinal affective computing: a different paradigm, but often the most appropriate