5,687 research outputs found
Heuristic usability evaluation on games: a modular approach
Heuristic evaluation is the preferred method to assess usability in games when experts conduct this
evaluation. Many heuristics guidelines have been proposed attending to specificities of games but
they only focus on specific subsets of games or platforms. In fact, to date the most used guideline to
evaluate games usability is still Nielsen’s proposal, which is focused on generic software. As a
result, most evaluations do not cover important aspects in games such as mobility, multiplayer
interactions, enjoyability and playability, etc. To promote the usage of new heuristics adapted to
different game and platform aspects we propose a modular approach based on the classification of
existing game heuristics using metadata and a tool, MUSE (Meta-heUristics uSability Evaluation
tool) for games, which allows a rebuild of heuristic guidelines based on metadata selection in order
to obtain a customized list for every real evaluation case. The usage of these new rebuilt heuristic
guidelines allows an explicit attendance to a wide range of usability aspects in games and a better
detection of usability issues. We preliminarily evaluate MUSE with an analysis of two different
games, using both the Nielsen’s heuristics and the customized heuristic lists generated by our tool.Unión Europea PI055-15/E0
Dynamic Hybrid Strategy Models for Networked Mulitplayer Games
Two of the primary factors in the development of
networked multiplayer computer games are network
latency and network bandwidth. Reducing the effects of
network latency helps maintain game-state fidelity,
while reducing network bandwidth usage increases the
scalability of the game to support more players. The
current technique to address these issues is to have each
player locally simulate remote objects (e.g. other
players). This is known as dead reckoning. Provided the
local simulations are accurate to within a given
tolerance, dead reckoning reduces the amount of
information required to be transmitted between players.
This paper presents an extension to the recently
proposed Hybrid Strategy Model (HSM) technique,
known as the Dynamic Hybrid Strategy Model
(DHSM). By dynamically switching between models of
user behaviour, the DHSM attempts to improve the
prediction capability of the local simulations, allowing
them to stay within a given tolerance for a longer
amount of time. This can lead to further reductions in
the amount of information required to be transmitted.
Presented results for the case of a simple first-person
shooter (FPS) game demonstrate the validity of the
DHSM approach over dead reckoning, leading to a
reduction in the number of state update packets sent and
indicating significant potential for network traffic
reduction in various multiplayer games/simulations
Toward an Ecology of Gaming
In her introduction to the Ecology of Games, Salen argues for the need for an increasingly complex and informed awareness of the meaning, significance, and practicalities of games in young people's lives. The language of the media is replete with references to the devil (and heavy metal) when it comes to the ill-found virtues of videogames, while a growing movement in K-12 education casts them as a Holy Grail in the uphill battle to keep kids learning. Her essay explores the different ways the volume's contributors add shades of grey to this often black-and-white mix, pointing toward a more sophisticated understanding of the myriad ways in which gaming could and should matter to those considering the future of learning
Load balancing for massively multiplayer online games
Supporting thousands, possibly hundreds of thousands, of players is a requirement that must be satisfied when delivering server based online gaming as a commercial concern. Such a requirement may be satisfied by utilising the cumulative processing resources afforded by a cluster of servers. Clustering of servers allow great flexibility, as the game provider may add servers to satisfy an increase in processing demands, more players, or remove servers for routine maintenance or upgrading. If care is not taken, the way processing demands are distributed across a cluster of servers may hinder such flexibility and also hinder player interaction within a game. In this paper we present an approach to load balancing that is simple and effective, yet maintains the flexibility of a cluster while promoting player interaction
Multistep-Ahead Neural-Network Predictors for Network Traffic Reduction in Distributed Interactive Applications
Predictive contract mechanisms such as dead reckoning are widely employed to support scalable
remote entity modeling in distributed interactive applications (DIAs). By employing a form of
controlled inconsistency, a reduction in network traffic is achieved. However, by relying on the
distribution of instantaneous derivative information, dead reckoning trades remote extrapolation
accuracy for low computational complexity and ease-of-implementation. In this article, we present
a novel extension of dead reckoning, termed neuro-reckoning, that seeks to replace the use of
instantaneous velocity information with predictive velocity information in order to improve the
accuracy of entity position extrapolation at remote hosts. Under our proposed neuro-reckoning
approach, each controlling host employs a bank of neural network predictors trained to estimate
future changes in entity velocity up to and including some maximum prediction horizon. The effect
of each estimated change in velocity on the current entity position is simulated to produce an
estimate for the likely position of the entity over some short time-span. Upon detecting an error
threshold violation, the controlling host transmits a predictive velocity vector that extrapolates
through the estimated position, as opposed to transmitting the instantaneous velocity vector. Such
an approach succeeds in reducing the spatial error associated with remote extrapolation of entity
state. Consequently, a further reduction in network traffic can be achieved. Simulation results
conducted using several human users in a highly interactive DIA indicate significant potential
for improved scalability when compared to the use of IEEE DIS standard dead reckoning. Our
proposed neuro-reckoning framework exhibits low computational resource overhead for real-time
use and can be seamlessly integrated into many existing dead reckoning mechanisms
For a learnable mathematics in the digital cultures
I begin with some general remarks concerning the co-evolution of representational forms and mathematical meanings. I then discuss the changed roles of mathematics and novel representations that emerge from the ubiquity of computational models, and briefly consider the implications for learning mathematics. I contend that a central component of knowledge required in modern societies involves the development of a meta-epistemological stance – i.e. developing a sense of mechanism for the models that underpin social and professional discourses. I illustrate this point in relation to recent research in which I am investigating the mathematical epistemology of engineering practice. Finally, I map out one implication for the design of future mathematical learning environments with reference to some data from the "Playground Project"
Multistep-Ahead Neural-Network Predictors for Network Traffic Reduction in Distributed Interactive Applications
Predictive contract mechanisms such as dead reckoning are widely employed to support scalable
remote entity modeling in distributed interactive applications (DIAs). By employing a form of
controlled inconsistency, a reduction in network traffic is achieved. However, by relying on the
distribution of instantaneous derivative information, dead reckoning trades remote extrapolation
accuracy for low computational complexity and ease-of-implementation. In this article, we present
a novel extension of dead reckoning, termed neuro-reckoning, that seeks to replace the use of
instantaneous velocity information with predictive velocity information in order to improve the
accuracy of entity position extrapolation at remote hosts. Under our proposed neuro-reckoning
approach, each controlling host employs a bank of neural network predictors trained to estimate
future changes in entity velocity up to and including some maximum prediction horizon. The effect
of each estimated change in velocity on the current entity position is simulated to produce an
estimate for the likely position of the entity over some short time-span. Upon detecting an error
threshold violation, the controlling host transmits a predictive velocity vector that extrapolates
through the estimated position, as opposed to transmitting the instantaneous velocity vector. Such
an approach succeeds in reducing the spatial error associated with remote extrapolation of entity
state. Consequently, a further reduction in network traffic can be achieved. Simulation results
conducted using several human users in a highly interactive DIA indicate significant potential
for improved scalability when compared to the use of IEEE DIS standard dead reckoning. Our
proposed neuro-reckoning framework exhibits low computational resource overhead for real-time
use and can be seamlessly integrated into many existing dead reckoning mechanisms
Inspection games in a mean field setting
In this paper, we present a new development of inspection games in a mean
field setting. In our dynamic version of an inspection game, there is one
inspector and a large number N interacting inspectees with a finite state
space. By applying the mean field game methodology, we present a solution as an
epsilon-equilibrium to this type of inspection games, where epsilon goes to 0
as N tends to infinity. In order to facilitate numerical analysis of this new
type inspection game, we conduct an approximation analysis, that is we
approximate the optimal Lipschitz continuous switching strategies by smooth
switching strategies. We show that any approximating smooth switching strategy
is also an epsilon-equilibrium solution to the inspection game with a large and
finite number N of inspectees with epsilon being of order 1/N
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