270 research outputs found
Virtual Reality Games for Motor Rehabilitation
This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion
Dead Reckoning Using Play Patterns in a Simple 2D Multiplayer Online Game
In today’s gaming world, a player expects the same play experience whether playing on a local network or online with many geographically distant players on congested networks. Because of delay and loss, there may be discrepancies in the simulated environment from player to player, likely resulting in incorrect perception of events. It is desirable to develop methods that minimize this problem. Dead reckoning is one such method. Traditional dead reckoning schemes typically predict a player’s position linearly by assuming players move with constant force or velocity. In this paper, we consider team-based 2D online action games. In such games, player movement is rarely linear. Consequently, we implemented such a game to act as a test harness we used to collect a large amount of data from playing sessions involving a large number of experienced players. From analyzing this data, we identified play patterns, which we used to create three dead reckoning algorithms. We then used an extensive set of simulations to compare our algorithms with the IEEE standard dead reckoning algorithm and with the recent “Interest Scheme” algorithm. Our results are promising especially with respect to the average export error and the number of hits
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
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
Network Traffic Adaptation For Cloud Games
With the arrival of cloud technology, game accessibility and ubiquity have a
bright future; Games can be hosted in a centralize server and accessed through
the Internet by a thin client on a wide variety of devices with modest
capabilities: cloud gaming. However, current cloud gaming systems have very
strong requirements in terms of network resources, thus reducing the
accessibility and ubiquity of cloud games, because devices with little
bandwidth and people located in area with limited and unstable network
connectivity, cannot take advantage of these cloud services. In this paper we
present an adaptation technique inspired by the level of detail (LoD) approach
in 3D graphics. It delivers multiple platform accessibility and network
adaptability, while improving user's quality of experience (QoE) by reducing
the impact of poor and unstable network parameters (delay, packet loss, jitter)
on game interactivity. We validate our approach using a prototype game in a
controlled environment and characterize the user QoE in a pilot experiment. The
results show that the proposed framework provides a significant QoE
enhancement
Rationale Document: Entity Information And Entity Interaction In A Distributed Interactive Simulation
Report on efforts to define and develop a standard communication protocol at the protocol data unit level
An Information-Based Dynamic Extrapolation Model for Networked Virtual Environments
Various Information Management techniques have been developed to help maintain a consistent shared virtual world in a
Networked Virtual Environment. However, such techniques have to be carefully adapted to the application state dynamics and
the underlying network. This work presents a novel framework that minimizes inconsistency by optimizing bandwidth usage to
deliver useful information. This framework measures the state evolution using an information model and dynamically switches
extrapolation models and the packet rate to make the most information-efficient usage of the available bandwidth. The results
shown demonstrate that this approach can help optimize consistency under constrained and time-varying network conditions
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