We collected mobility traces of 84,208 avatars spanning 22 regions over two months in Second Life, a popular networked virtual environment. We analyzed the traces to characterize the dynamics of the avatars mobility and behavior, both temporally and spatially. We discuss the implications of the our findings to the design o f peer-to-peer networked virtual environments, interest management, mobility modeling of avatars, server load balancing and zone partitioning, client-side caching, and prefetching.
@cite_5 and @cite_26 collected traces from Quake III, a popular, multi-player, first person shooting (FPS) game and developed mobility models to describe the movement of the players. @cite_3 collected a large trace, comparable in scale to ours, of players movement from World of Warcraft (WoW), a massively multi-player online role playing game (MMORPG), and analyzed the dynamics of the populations, players arrival departure rate, session length, player distribution, and player movements. FPS games and MMORPGs have different characteristics than NVEs. Players in fast-action, FPS games tend to move around constantly. In MMORPGs, players usually engage in quests to gain level and new abilities. Players tend to gather in a location for an event (e.g. new monsters to fight) and disperse afterwards. Players also tend to move in groups. We observed a different pattern for NVEs.
@cite_5 @cite_26 @cite_3
2083589344 1585981768 2021955144
Modern Massively Multiplayer Online Role-Playing Games (MMORPGs) provide lifelike virtual environments in which players can conduct a variety of activities including combat, trade, and chat with other players. While the game world and the available actions therein are inspired by their offline counterparts, the games' popularity and dedicated fan base are testaments to the allure of novel social interactions granted to people by allowing them an alternative life as a new character and persona. In this paper we investigate the phenomenon of "gender swapping," which refers to players choosing avatars of genders opposite to their natural ones. We report the behavioral patterns observed in players of Fairyland Online, a globally serviced MMORPG, during social interactions when playing as in-game avatars of their own real gender or gender-swapped. We also discuss the effect of gender role and self-image in virtual social situations and the potential of our study for improving MMORPG quality and detecting online identity frauds. This study examines the usability challenges faced by new players of massively multiplayer online role-playing games (MMORPGs), one of the fastest-growing segments of the video game industry. Played in completely online worlds, these games allow players to communicate with one another, form groups and communities, and compete in a variety of fantasy environments.Nineteen subjects participated in an exploratory usability study of four games, three MMORPGs and a similar single-player game used for comparison. Results reveal that many people not usually considered as potential players of these games may be interested in them, but a wide variety of usability issues present serious problems for players inexperienced with the genre. Based on an analysis of the usability data and player feedback, specific recommendations are made to improve the experience of these games for new players. These results further demonstrate the applicability and importance of usability testing to video games. World of Warcraft (WoW) is one of the most popular massively multiplayer games (MMOs) to date, with more than 6 million subscribers worldwide. This article uses data collected over 8 months with automated “bots” to explore how WoW functions as a game. The focus is on metrics reflecting a player’s gaming experience: how long they play, the classes and races they prefer, and so on. The authors then discuss why and how players remain committed to this game, how WoW’s design partitions players into groups with varying backgrounds and aspirations, and finally how players “consume” the game’s content, with a particular focus on the endgame at Level 60 and the impact of player-versus-player-combat. The data illustrate how WoW refined a formula inherited from preceding MMOs. In several places, it also raises questions about WoW’s future growth and more generally about the ability of MMOs to evolve beyond their familiar template. In this paper, we propose a new distributed event delivery method for MMORPG (Massively Multiplayer Online Role Playing Games). In our method, the whole game space is divided into multiple sub spaces with the same size and some player nodes are selected as responsible nodes to deliver game events occurring in their responsible sub spaces. Our method includes (1) a load balancing mechanism which allows each responsible node for the crowded sub space to dynamically construct a tree of multiple nodes and deliver events along the tree to reduce event forwarding overhead per node, (2) a technique to reduce end-to-end event delivery delay by dynamically replacing nodes in the tree, and (3) a technique to efficiently and seamlessly switch sub spaces to be observed while each player's view moves around in the game space. Through experiments, we show that our method achieves practical performance for MMORPG. Understanding the distributions and behaviors of players within Massively Multiplayer Online Games (MMOGs) is essential for research in scalable architectures for these systems. We provide the first look into this problem through a measurement study on one of the most popular MMOGs, World of Warcraft [15]. Our goal is to answer four fundamental questions: how does the population of the virtual world change over time, how are players distributed in the virtual world, how much churn occurs with players, and how do they move in the virtual world. Through probing-based measurements, our preliminary results show that populations fluctuate according to a prime-time schedule, player distribution and churn appears to occur on a power-law distribution, and players move to only a small number of zones during each playing session. The ultimate goal of our research is to design an accurate player model for MMOGs so that future research can predict and simulate player behavior and population fluctuations over time. We consider multi-player repeated games involving a large number of players with large strategy spaces and enmeshed utility structures. In these ldquolarge-scalerdquo games, players are inherently faced with limitations in both their observational and computational capabilities. Accordingly, players in large-scale games need to make their decisions using algorithms that accommodate limitations in information gathering and processing. This disqualifies some of the well known decision making models such as ldquoFictitious Playrdquo (FP), in which each player must monitor the individual actions of every other player and must optimize over a high dimensional probability space. We will show that Joint Strategy Fictitious Play (JSFP), a close variant of FP, alleviates both the informational and computational burden of FP. Furthermore, we introduce JSFP with inertia, i.e., a probabilistic reluctance to change strategies, and establish the convergence to a pure Nash equilibrium in all generalized ordinal potential games in both cases of averaged or exponentially discounted historical data. We illustrate JSFP with inertia on the specific class of congestion games, a subset of generalized ordinal potential games. In particular, we illustrate the main results on a distributed traffic routing problem and derive tolling procedures that can lead to optimized total traffic congestion. Many online games have their own ecosystems, where players can purchase in-game assets using game money. Players can obtain game money through active participation or “real money trading” through official channels: converting real money into game money. The unofficial market for real money trading gave rise to gold farming groups (GFGs), a phenomenon with serious impact in the cyber and real worlds. GFGs in massively multiplayer online role-playing games (MMORPGs) are some of the most interesting underground cyber economies because of the massive nature of the game. To detect GFGs, there have been various studies using behavioral traits. However, they can only detect gold farmers, not entire GFGs with internal hierarchies. Even worse, GFGs continuously develop techniques to hide, such as forming front organizations, concealing cyber-money, and changing trade patterns when online game service providers ban GFGs. In this paper, we analyze the characteristics of the ecosystem of a large-scale MMORPG, and devise a method for detecting GFGs. We build a graph that characterizes virtual economy transactions, and trace abnormal trades and activities. We derive features from the trading graph and physical networks used by GFGs to identify them in their entirety. Using their structure, we provide recommendations to defend effectively against GFGs while not affecting the existing virtual ecosystem. In this study, we propose a comprehensive performance management tool for measuring and reporting operational activities of game players. This study uses performance data of game players in EverQuest II, a popular MMORPG developed by Sony Online Entertainment, to build performance prediction models for game players. The prediction models provide a projection of player’s future performance based on his past performance, which is expected to be a useful addition to existing player performance monitoring tools. First, we show that variations of PECOTA [2] and MARCEL [3], two most popular baseball home run prediction methods, can be used for game player performance prediction. Second, we evaluate the effects of varying lengths of past performance and show that past performance can be a good predictor of future performance up to a certain degree. Third, we show that game players do not regress towards the mean and that prediction models built on buckets using discretization based on binning and histograms lead to higher prediction coverage. In this study, we propose a comprehensive performance management tool for measuring and reporting operational activities of game players This study uses performance data of game players in EverQuest II, a popular MMORPG developed by Sony Online Entertainment, to build performance prediction models for game players The prediction models provide a projection of player's future performance based on his past performance, which is expected to be a useful addition to existing player performance monitoring tools First, we show that variations of PECOTA [2] and MARCEL [3], two most popular baseball home run prediction methods, can be used for game player performance prediction Second, we evaluate the effects of varying lengths of past performance and show that past performance can be a good predictor of future performance up to a certain degree Third, we show that game players do not regress towards the mean and that prediction models built on buckets using discretization based on binning and histograms lead to higher prediction coverage. We analyze mechanisms of player retention and commitment in massively multiplayer online games. Our ground assumptions on player retention are based on a marketing model of customer retention and commitment. To measure the influence of gameplay, in-game sociality, and real-life status on player commitment, we use the following metrics: weekly play time, stop rate and number of years respondents have been playing the game. The cross-cultural sample is composed of 2865 World of Warcraft players from North-America, Europe, Taiwan, and Hong-Kong who completed an online questionnaire. We differentiate players in terms of demographic categories including age, region, gender and marital status. We examine the social behaviors of game experts in Everquest II, a popular massive multiplayer online role-playing game (MMO). We rely on Exponential Random Graph Models (ERGM) to examine the anonymous privacy-protected social networks of 1,457 players over a five-day period. We find that those who achieve the most in the game send and receive more communication, while those who perform the most efficiently at the game show no difference in communication behavior from other players. Both achievement and performance experts tend to communicate with those at similar expertise levels, and higher-level experts are more likely to receive communication from other players. Massively Multiplayer Online Games (MMOGs) routinely attract millions of players but little empirical data is available to assess their players' social experiences. In this paper, we use longitudinal data collected directly from the game to examine play and grouping patterns in one of the largest MMOGs: World of Warcraft. Our observations show that the prevalence and extent of social activities in MMOGs might have been previously over-estimated, and that gaming communities face important challenges affecting their cohesion and eventual longevity. We discuss the implications of our findings for the design of future games and other online social spaces. Estimating affective and cognitive states in conditions of rich human-computer interaction, such as in games, is a field of growing academic and commercial interest. Entertainment and serious games can benefit from recent advances in the field as, having access to predictors of the current state of the player (or learner) can provide useful information for feeding adaptation mechanisms that aim to maximize engagement or learning effects. In this paper, we introduce a large data corpus derived from 58 participants that play the popular Super Mario Bros platform game and attempt to create accurate models of player experience for this game genre. Within the view of the current research, features extracted both from player gameplay behavior and game levels, and player visual characteristics have been used as potential indicators of reported affect expressed as pairwise preferences between different game sessions. Using neuroevolutionary preference learning and automatic feature selection, highly accurate models of reported engagement, frustration, and challenge are constructed (model accuracies reach 91 , 92 , and 88 for engagement, frustration, and challenge, respectively). As a step further, the derived player experience models can be used to personalize the game level to desired levels of engagement, frustration, and challenge as game content is mapped to player experience through the behavioral and expressivity patterns of each player. In order to promote computer Go and stimulate further development and research in the field, the event activities, Computational Intelligence Forum and World 9times9 Computer Go Championship, were held in Taiwan. This study focuses on the invited games played in the tournament Taiwanese Go players versus the computer program MoGo held at the National University of Tainan (NUTN), Tainan, Taiwan. Several Taiwanese Go players, including one 9-Dan (9D) professional Go player and eight amateur Go players, were invited by NUTN to play against MoGo from August 26 to October 4, 2008. The MoGo program combines all-moves-as-first (AMAF) rapid action value estimation (RAVE) values, online "upper confidence tree (UCT)-like" values, offline values extracted from databases, and expert rules. Additionally, four properties of MoGo are analyzed including: (1) the weakness in corners, (2) the scaling over time, (3) the behavior in handicap games, and (4) the main strength of MoGo in contact fights. The results reveal that MoGo can reach the level of 3 Dan (3D) with: (1) good skills for fights, (2) weaknesses in corners, in particular, for "semeai" situations, and (3) weaknesses in favorable situations such as handicap games. It is hoped that the advances in AI and computational power will enable considerable progress in the field of computer Go, with the aim of achieving the same levels as computer chess or Chinese chess in the future. How do video game skills develop, and what sets the top players apart? We study this question of skill through a rating generated from repeated multiplayer matches called TrueSkill. Using these ratings from 7 months of games from over 3 million players, we look at how play intensity, breaks in play, skill change over time, and other games affect skill. These analyzed factors are then combined to model future skill and games played; the results show that skill change in early matches is a useful metric for modeling future skill, while play intensity explains eventual games played. The best players in the 7-month period, who we call "Master Blasters", have varied skill patterns that often run counter to the trends we see for typical players. The data analysis is supplemented with a 70 person survey to explore how players' self-perceptions compare to the gameplay data; most survey responses align well with the data and provide insight into player beliefs and motivation. Finally, we wrap up with a discussion about hiding skill information from players, and implications for game designers. Strategic games may exhibit symmetries in a variety of ways. A characteristic feature, enabling the compact representation of games even when the number of players is unbounded, is that players cannot, or need not, distinguish between the other players. We investigate the computational complexity of pure Nash equilibria in four classes of symmetric games obtained by considering two additional properties: identical payoff functions for all players and the ability to distinguish oneself from the other players. In contrast to other types of succinctly representable multi-player games, the pure equilibrium problem is tractable in all four classes when only a constant number of actions is available to each player. Identical payoff functions make the difference between TC^0-completeness and membership in AC^0, while a growing number of actions renders the equilibrium problem NP-hard for three of the classes and PLS-hard for the most restricted class for which the existence of a pure equilibrium is guaranteed. Our results also extend to larger classes of threshold symmetric games where players are unable to determine the exact number of players playing a certain action. We present an approach to support massively multiplayer games on peer-to-peer overlays. Our approach exploits the fact that players in MMGs display locality of interest, and therefore can form self-organizing groups based on their locations in the virtual world. To this end, we have designed scalable mechanisms to distribute the game state to the participating players and to maintain consistency in the face of node failures. The resulting system dynamically scales with the number of online players. It is more flexible and has a lower deployment cost than centralized games servers. We have implemented a simple game we call SimMud, and experimented with up to 4000 players to demonstrate the applicability of this approach. In this paper, we report findings from an exploratory study of player and team performance in Halo 3, a popular First-Person-Shooter game developed by Bungie. In the study, we first analyze player and team statistics obtained from the 2008 and 2009 seasons for professional Halo 3 games in order to investigate the impact of change in team composition on player and team performance. We then examine the impact of past performance on future performance of players and teams. Performing a large-scale experiment on a real-world dataset, we observe that player and team performance can be predicted with fairly high accuracy and that information about change in team composition can further improve the prediction results. Developing computer-controlled groups to engage in combat, control the use of limited resources, and create units and buildings in real-time strategy (RTS) games is a novel application in game AI. However, tightly controlled online commercial game pose challenges to researchers interested in observing player activities, constructing player strategy models, and developing practical AI technology in them. Instead of setting up new programming environments or building a large amount of agentpsilas decision rules by playerpsilas experience for conducting real-time AI research, the authors use replays of the commercial RTS game StarCraft to evaluate human player behaviors and to construct an intelligent system to learn human-like decisions and behaviors. A case-based reasoning approach was applied for the purpose of training our system to learn and predict player strategies. Our analysis indicates that the proposed system is capable of learning and predicting individual player strategies, and that players provide evidence of their personal characteristics through their building construction order. Skat is Germany's national card game played by millions of players around the world. In this paper, we present the world's first computer skat player that plays at the level of human experts. This performance is achieved by improving state evaluations using game data produced by human players and by using these state evaluations to perform inference on the unobserved hands of opposing players. Our results demonstrate the gains from adding inference to an imperfect information game player and show that training on data from average human players can result in expert-level playing strength.