29 research outputs found

    Dead Reckoning Using Play Patterns in a Simple 2D Multiplayer Online Game

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    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

    A Stochastic Model of Plausibility in Live-Virtual-Constructive Environments

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    Distributed live-virtual-constructive simulation promises a number of benefits for the test and evaluation community, including reduced costs, access to simulations of limited availability assets, the ability to conduct large-scale multi-service test events, and recapitalization of existing simulation investments. However, geographically distributed systems are subject to fundamental state consistency limitations that make assessing the data quality of live-virtual-constructive experiments difficult. This research presents a data quality model based on the notion of plausible interaction outcomes. This model explicitly accounts for the lack of absolute state consistency in distributed real-time systems and offers system designers a means of estimating data quality and fitness for purpose. Experiments with World of Warcraft player trace data validate the plausibility model and exceedance probability estimates. Additional experiments with synthetic data illustrate the model\u27s use in ensuring fitness for purpose of live-virtual-constructive simulations and estimating the quality of data obtained from live-virtual-constructive experiments

    Dead Reckoning for Distributed Network Online Games

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    Online networked games are becoming increasingly popular. One type of network architecture used in these games is a distributed network architecture, where players send periodic updates to each other and each player must locally reconstruct the position of their opponents in between these updates. In this work, we assume a car model for the players, as errors in this type of network are most pronounced when players have high speeds. We are interested in decreasing this update frequency in order to conserve bandwidth. We are also interested in investigating issues that arise when these locally replicated opponents need to interact and collide with objects in the environment. In this thesis we decompose the replication problem into two components: first, we must predict the position of our opponents by extrapolating from the received updates, then we must create a smooth trajectory from these predicted positions that appears believable to the player. We introduce a neural network based approach to solving the prediction portion that outperforms the current state of the art. We then propose a neural network based approach and an approach based on a path tracking controller for mobile robots to generate smooth trajectories. We present results to compare these approaches and show that the path tracking approach performs better than both the neural network approach and the established state of the art approaches. We also investigate collisions between replicated opponents and the environment. This is a complex problem, so for simplicity we are only examining collisions with static obstacles. Collisions can vary dramatically based on small changes in impact point and angle, and so we want to be able to predict collisions based on the predicted position of the opponent because that is theoretically our best estimate of the true position of our opponent. We propose a neural network based approach to this problem, which is able to predict the collision response of a vehicle colliding with a static obstacle. We present results that show this method has potential to outperform the current best practice, but we also discuss several implementation issues that must be addressed

    Study and implementation of a real time online football game for mobile devices

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    The main goal of this project is extracting an optimized system to do a sensible application to delay loses and jitter in a wireless environment. This system will be used in a football game for mobile devices with maximum two players per game

    Middleware services for distributed virtual environments

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    PhD ThesisDistributed Virtual Environments (DVEs) are virtual environments which allow dispersed users to interact with each other and the virtual world through the underlying network. Scalability is a major challenge in building a successful DVE, which is directly affected by the volume of message exchange. Different techniques have been deployed to reduce the volume of message exchange in order to support large numbers of simultaneous participants in a DVE. Interest management is a popular technique for filtering unnecessary message exchange between users. The rationale behind interest management is to resolve the "interests" of users and decide whether messages should be exchanged between them. There are three basic interest management approaches: region-based, aura-based and hybrid approaches. However, if the time taken for an interest management approach to determine interests is greater than the duration of the interaction, it is not possible to guarantee interactions will occur correctly or at all. This is termed the Missed Interaction Problem, which all existing interest management approaches are susceptible to. This thesis provides a new aura-based interest management approach, termed Predictive Interest management (PIM), to alleviate the missed interaction problem. PIM uses an enlarged aura to detect potential aura-intersections and iii initiate message exchange. It utilises variable message exchange frequencies, proportional to the intersection degree of the objects' expanded auras, to restrict bandwidth usage. This thesis provides an experimental system, the PIM system, which couples predictive interest management with the de-centralised server communication model. It utilises the Common Object Request Broker Architecture (CORBA) middleware standard to provide an interoperable middleware for DVEs. Experimental results are provided to demonstrate that PIM provides a scalable interest management approach which alleviates the missed interaction problem

    Middleware services for distributed virtual environments

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    PhD ThesisDistributed Virtual Environments (DVEs) are virtual environments which allow dispersed users to interact with each other and the virtual world through the underlying network. Scalability is a major challenge in building a successful DVE, which is directly affected by the volume of message exchange. Different techniques have been deployed to reduce the volume of message exchange in order to support large numbers of simultaneous participants in a DVE. Interest management is a popular technique for filtering unnecessary message exchange between users. The rationale behind interest management is to resolve the "interests" of users and decide whether messages should be exchanged between them. There are three basic interest management approaches: region-based, aura-based and hybrid approaches. However, if the time taken for an interest management approach to determine interests is greater than the duration of the interaction, it is not possible to guarantee interactions will occur correctly or at all. This is termed the Missed Interaction Problem, which all existing interest management approaches are susceptible to. This thesis provides a new aura-based interest management approach, termed Predictive Interest management (PIM), to alleviate the missed interaction problem. PIM uses an enlarged aura to detect potential aura-intersections and iii initiate message exchange. It utilises variable message exchange frequencies, proportional to the intersection degree of the objects' expanded auras, to restrict bandwidth usage. This thesis provides an experimental system, the PIM system, which couples predictive interest management with the de-centralised server communication model. It utilises the Common Object Request Broker Architecture (CORBA) middleware standard to provide an interoperable middleware for DVEs. Experimental results are provided to demonstrate that PIM provides a scalable interest management approach which alleviates the missed interaction problem

    Effects of Local Latency on Games

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    Video games are a major type of entertainment for millions of people, and feature a wide variety genres. Many genres of video games require quick reactions, and in these games it is critical for player performance and player experience that the game is responsive. One of the major contributing factors that can make games less responsive is local latency — the total delay between input and a resulting change to the screen. Local latency is produced by a combination of delays from input devices, software processing, and displays. Due to latency, game companies spend considerable time and money play-testing their games to ensure the game is both responsive and that the in-game difficulty is reasonable. Past studies have made it clear that local latency negatively affects both player performance and experience, but there is still little knowledge about local latency’s exact effects on games. In this thesis, we address this problem by providing game designers with more knowledge about local latency’s effects. First, we performed a study to examine latency’s effects on performance and experience for popular pointing input devices used with games. Our results show significant differences between devices based on the task and the amount of latency. We then provide design guidelines based on our findings. Second, we performed a study to understand latency’s effects on ‘atoms’ of interaction in games. The study varied both latency and game speed, and found game speed to affect a task’s sensitivity to latency. Third, we used our findings to build a model to help designers quickly identify latency-sensitive game atoms, thus saving time during play-testing. We built and validated a model that predicts errors rates in a game atom based on latency and game speed. Our work helps game designers by providing new insight into latency’s varied effects and by modelling and predicting those effect
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