5,554 research outputs found
Modelling player preferences in AR mobile games
© 2019 IEEE. In this paper, we use preference learning techniques to model players' emotional preferences in an AR mobile game. This exploratory study uses player behaviour to make these preference predictions. The described techniques successfully predict players' frustration and challenge levels with high accuracy while all other preferences tested (boredom, excitement and fun) perform better than random chance. This paper describes the AR treasure hunt game we developed, the user study conducted to collect player preference data, analysis performed, and preference learning techniques applied to model this data. This work is motivated to personalize players' experiences by using these computational models to optimize content creation and game balancing systems in these environments. The generality of our technique, limitations, and usability as a tool for personalization of AR mobile games is discussed
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Prototyping a Context-Aware Framework for Pervasive Entertainment Applications
Experience-driven MAR games: Personalising Mobile Augmented Reality games using Player Models
PhD ThesesWe are witnessing an unprecedented growth of Mobile Augmented Reality
(MAR) technologies, one of the main research areas being MAR games. While
this field is still in its early days, researchers have shown the physical health
benefits of playing these type of games. Computational models have been used
in traditional (non-AR) digital games to predict player experience (PX). These
models give designers insights about PX, and can also be used within games for
real-time adaptation or personalised content generation. Following these findings,
this thesis investigates the potential of creating models that use movement
data and game metrics to predict PX.
An initial pilot study is conducted to evaluate the use of movement data
and game metrics to predict players’ emotional preferences between different
game levels of an exploration-based MAR game. Results indicate that emotional
preferences regarding frustration (≈ 93%) and challenge (≈ 93%) can
be predicted to a reliable and reasonable degree of accuracy. To determine if
these techniques can be applied to serious games for health, an AR exergame
is developed for experiments two, three and four of this thesis. The second and
third experiments aim to predict key experiential constructs, player competence
and immersion, that are important to PX. These experiments further validate
the use of movement data and game metrics to model different aspects of PX
in MAR games. Results suggest that players’ competence (≈ 73%) and sense
of mastery (≈ 81%) can be predicted to a reasonable degree of accuracy. For
the final experiment, this mastery model is used to create a dynamic difficulty
adaptation (DDA) system. The adaptive exergame is then evaluated against
a non-adaptive variant of the same game. Results indicate that the adaptive
game makes players feel a higher sense of confidence during gameplay and that
the adaptation mechanics are more effective for players who do not engage in
regular physical activity.
Across the four studies presented, this thesis is the first known research activity
that investigates using movement data and game metrics to model PX
for DDA in MAR games and makes the following novel contributions: i) movement
data and game metrics can be used to predict player’s sense of mastery or
competence reliably compared to other aspects of PX tested, ii) mastery-based
game adaptation makes players feel greater confidence during game-play, and iii)
mastery-based game adaptation is more effective for players who do not engage
in physical activity. This work also presents a new methodology for PX prediction
in MAR games and a novel adaptation engine driven by player mastery. In
summary, this thesis proposes that PX modelling can be successfully applied to
MAR games, especially for DDA which results in a highly personalised PX and
shows potential as a tool for increasing physical activity
Affective Game Computing: A Survey
This paper surveys the current state of the art in affective computing
principles, methods and tools as applied to games. We review this emerging
field, namely affective game computing, through the lens of the four core
phases of the affective loop: game affect elicitation, game affect sensing,
game affect detection and game affect adaptation. In addition, we provide a
taxonomy of terms, methods and approaches used across the four phases of the
affective game loop and situate the field within this taxonomy. We continue
with a comprehensive review of available affect data collection methods with
regards to gaming interfaces, sensors, annotation protocols, and available
corpora. The paper concludes with a discussion on the current limitations of
affective game computing and our vision for the most promising future research
directions in the field
Unionization in a dynamic oligopolistic model of international trade.
The study of dynamic strategic behavior in international trade environments with imperfect factor markets (unions) yields significantly different policy implications compared to those that obtain under static settings. We find that contrary to static equilibria, the equilibrium of our model exhibits renegotiation-proofness; unilateral implementation of cost subsidies may yield negative domestic welfare effects; and trade policy tools are not useful in pursuing rent-shifting objectives.
Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches
Peer-to-peer (P2P) energy trading has emerged as a next-generation energy
management mechanism for the smart grid that enables each prosumer of the
network to participate in energy trading with one another and the grid. This
poses a significant challenge in terms of modeling the decision-making process
of each participant with conflicting interest and motivating prosumers to
participate in energy trading and to cooperate, if necessary, for achieving
different energy management goals. Therefore, such decision-making process
needs to be built on solid mathematical and signal processing tools that can
ensure an efficient operation of the smart grid. This paper provides an
overview of the use of game theoretic approaches for P2P energy trading as a
feasible and effective means of energy management. As such, we discuss various
games and auction theoretic approaches by following a systematic classification
to provide information on the importance of game theory for smart energy
research. Then, the paper focuses on the P2P energy trading describing its key
features and giving an introduction to an existing P2P testbed. Further, the
paper zooms into the detail of some specific game and auction theoretic models
that have recently been used in P2P energy trading and discusses some important
finding of these schemes.Comment: 38 pages, single column, double spac
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