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
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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. 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. This paper proposes the Networked Game Mobility Model (NGMM), for synthesising mobility in First-Person-Shooter (FPS) networked games. Current networked game research focuses on modelling low-level aspects, such as packet inter-arrival times and packet sizes, to optimise network traffic and efficient use of gaming servers. Due to the increasing popularity of multiplayer online games, the need has arisen to develop more realistic models. NGMM is such a model that utilises application level aspects of networked game traces to statistically model FPS games. It is believed that an understanding of the application level aspect (e.g. mobility and user actions) of the network is necessary to derive the causality of increasing workloads on the servers, particularly in response to increasing online game popularity. To evaluate the performance of the model simulations, comparisons are made between the original game traces, the Random Way Point Model and NGMM. Analyses of the comparative simulation results show that NGMM is capable of closely matching actual game traces. The incorporation of application level knowledge, performance boundaries of current optimisation techniques, including dead-reckoning and interest management, are also effectively ascertained in this research. This is particularly significant as current models are unable to evaluate their impact with optimisation techniques. 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. 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. Multiplayer games played over the Internet have become very popular in the lastfew years. An interesting subcategory are the so-called massively multiplayeronline games (MMOGs) that allow thousands of player characters to share asingle game world. Such a world is usually run on a high-performance and high-availability server cluster. However, even with games that have been extensivelybeta-tested, downtimes of several hours because of hard- or software failures arenot uncommon. Downtimes, especially in the ﬁrst few weeks after the release,can negatively aﬀect the image of the game and the company that created it.Traditionally, a cluster of servers contains one virtual world of a MMOG.Such infrastructure is inﬂexible and error-prone. One would rather like to havea system that allows disconnecting a server at runtime while others take overits tasks. Server-based MMOGs can have performance problems if players areconcentrated in certain parts of the game world or some worlds are overpopu-lated. Thus, there is also a need for load balancing mechanisms. Peer-to-Peer(P2P) systems quite naturally support the use of load balancing.In this paper we use a structured P2P technology for the organization of theinfrastructure and thus for the reduction of downtimes in MMOGs. We splitthe game world in disjunctive rectangular zones and distribute them on diﬀerentnodes of the P2P network.Online games are an interesting challenge and chance for the future devel-opment of the P2P paradigm. A wide variety of aspects of only theoreticallysolved and especially yet completely unsolved problems are covered by this ap-plication. Security and trust problems appear as well as the need to preventcheating. The application is not as tolerant to faults as instant messaging or ﬁlesharing. Consistent data storage is a problem, decisions and transactions haveto be performed in a decentralized way. Moreover, the P2P network is not usedas pure lookup service, but more as a communication and application-speciﬁcsocial structure.The rest of this paper is organized as follows: First we discuss related work inSection 2 and give a brief introduction to P2P and MMOGs and their challengesin Section 3. Section 4 shows our approach to use structured P2P Systems forMMOGs and section 5 the evaluation with player traces from a real MMOG.Finally, Section 6 provides conclusions. Real-time strategy (RTS) games, such as Blizzard's StarCraft, are fast paced war simulation games in which players have to manage economies, control many dozens of units, and deal with uncertainty about opposing unit locations in real-time. Even in perfect information settings, constructing strong AI systems has been difficult due to enormous state and action spaces and the lack of good state evaluation functions and high-level action abstractions. To this day, good human players are still handily defeating the best RTS game AI systems, but this may change in the near future given the recent success of deep convolutional neural networks (CNNs) in computer Go, which demonstrated how networks can be used for evaluating complex game states accurately and to focus look-ahead search. In this paper we present a CNN for RTS game state evaluation that goes beyond commonly used material based evaluations by also taking spatial relations between units into account. We evaluate the CNN's performance by comparing it with various other evaluation functions by means of tournaments played by several state-of-the-art search algorithms. We find that, despite its much slower evaluation speed, on average the CNN based search performs significantly better compared to simpler but faster evaluations. These promising initial results together with recent advances in hierarchical search suggest that dominating human players in RTS games may not be far off. The most important asset of a Massively Multiplayer Online Game is its world state, as it represents the combined efforts and progress of all its participants. Thus, it is extremely important that this state is not lost in case of server failures. Survival of the world state is typically achieved by making it persistent, e.g., by storing it in a relational database. The main challenge of this approach is to track the large volume of modifications applied to the world in real time. This paper compares a variety of strategies to persist changes of the game world. While critical events must be written synchronously to the persistent storage, a set of approximation strategies are discussed and compared that are suitable for events with low consistency requirements, such as player movements. An analysis to better understand the possible limitations and bottlenecks of these strategies is presented using experimental data from an MMOG research framework. Our analysis shows that a distance-based solution offers the scalability and efficiency required for large-scale games as well as offering error bounds and eliminating unnecessary updates associated with localized movement. In massively multiplayer online role-playing games (MMORPGs), each race holds some attributes and skills. Each skill contains several abilities such as physical damage and hit rate. All those attributes and abilities are functions of the character's level, which are called Ability-Increasing Functions (AIFs). A well-balanced MMORPG is characterized by having a set of well-balanced AIFs. In this paper, we propose a coevolutionary design method, including integration with the modified probabilistic incremental program evolution (PIPE) and the cooperative coevolutionary algorithm (CCEA), to solve the balance problem of MMORPGs. Moreover, we construct a simplest turn-based game model and perform a series of experiments based on it. The results indicate that the proposed method is able to obtain a set of well-balanced AIFs more efficiently, compared with the simple genetic algorithm (SGA), the simulated annealing algorithm (SAA) and the hybrid discrete particle swarm optimization (HDPSO) algorithm. The results also show that the performance of PIPE has been significantly improved through the modification works. In Real-Time Strategy (RTS) video games, players (controlled by humans or computers) build structures and recruit armies, fight for space and resources in order to control strategic points, destroy the opposing force and ultimately win the game. Players need to predict where and how the opponents will strike in order to best defend themselves. Conversely, assessing how the opponents will defend themselves is crucial to mounting a successful attack while exploiting the vulnerabilities in the opponent's defence strategy. In this context, to be truly adaptable, computer-controlled players need to recognize their opponents' behaviour, their goals, and their plans to achieve those goals. In this paper we analyze the algorithmic challenges behind behaviour recognition in RTS games and discuss a generic RTS behaviour recognition system that we are developing to address those challenges. The application domain is that of RTS games, but many of the key points we discuss also apply to other video game genres such as multiplayer first person shooter (FPS) games. 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. 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. 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. Multiplayer Online Battle Arena (MOBA) games are among the most played digital games in the world. In these games, teams of players fight against each other in arena environments, and the gameplay is focussed on tactical combat. In this paper, we present three data-driven measures of spatio-temporal behaviour in Defence of the Ancients 2 (DotA 2): 1) Zone changes; 2) Distribution of team members and: 3) Time series clustering via a fuzzy approach. We present a method for obtaining accurate positional data from DotA 2. We investigate how behaviour varies across these measures as a function of the skill level of teams, using four tiers from novice to professional players. Results from three analyses indicate that spatio-temporal behaviour of MOBA teams is highly related to team skill. 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. Massively multiplayer online games (MMOGs) can be fascinating laboratories to observe group dynamics online. In particular, players must form persistent associations or "guilds" to coordinate their actions and accomplish the games' toughest objectives. Managing a guild, however, is notoriously difficult and many do not survive very long. In this paper, we examine some of the factors that could explain the success or failure of a game guild based on more than a year of data collected from five World of Warcraft servers. Our focus is on structural properties of these groups, as represented by their social networks and other variables. We use this data to discuss what games can teach us about group dynamics online and, in particular, what tools and techniques could be used to better support gaming communities. We attack the problem of controlling teams of autonomous units during skirmishes in real-time strategy games. Earlier work had shown promise in evolving control algorithm parameters that lead to high performance team behaviors similar to those favored by good human players in real-time strategy games like Starcraft. This algorithm specifically encoded parameterized kiting and fleeing behaviors and the genetic algorithm evolved these parameter values. In this paper we investigate using influence maps and potential fields alone to compactly represent and control real-time team behavior for entities that can maneuver in three dimensions. A two-objective fitness function that maximizes damage done and minimizes damage taken guides our multi-objective evolutionary algorithm. Preliminary results indicate that evolving friend and enemy unit potential field parameters for distance, weapon characteristics, and entity health suffice to produce complex, high performing, three-dimensional, team tactics. StarCraft is a real-time strategy (RTS) game and the choice of strategy has big impact on the final results of the game. For human players, the most important thing in the game is to select the strategy in the early stage of the game. Also, it is important to recognize the opponent's strategy as quickly as possible. Because of the “fog-of-war” in the game, the player should send a scouting unit to opponent's hidden territory and the player predicts the types of strategy from the partially observed information. Usually, expert players are familiar with the relationships between two build orders and they can change the current build order if his choice is not strong to the opponent's strategy. However, players in AI competitions show quite different behaviors compared to the human leagues. For example, they usually have a pre-selected build order and rarely change their order during the game. In fact, the computer players have little interest in recognizing opponent's strategy and scouting units are used in a limited manner. The reason is that the implementation of scouting behavior and the change of build order from the scouting vision is not a trivial problem. In this paper, we propose to use replays to predict the strategy of players and make decision on the change of build orders. Experimental results on the public replay files show that the proposed method predicts opponent's strategy accurately and increases the chance of winning in the game. In this paper, we propose a novel framework for training vision-based agent for First-Person Shooter (FPS) Game, in particular Doom. Our framework combines the state-of-the-art reinforcement learning approach (Asynchronous Advantage Actor-Critic (A3C) model) with curriculum learning. Our model is simple in design and only uses game states from the AI side, rather than using opponents' information. On a known map, our agent won 10 out of the 11 attended games and the champion of Track1 in ViZDoom AI Competition 2016 by a large margin, 35 higher score than the second place. Team-based invasion sports such as football, basketball, and hockey are similar in the sense that the players are able to move freely around the playing area and that player and team performance cannot be fully analysed without considering the movements and interactions of all players as a group. State-of-the-art object tracking systems now produce spatio-temporal traces of player trajectories with high definition and high frequency, and this, in turn, has facilitated a variety of research efforts, across many disciplines, to extract insight from the trajectories. We survey recent research efforts that use spatio-temporal data from team sports as input and involve non-trivial computation. This article categorises the research efforts in a coherent framework and identifies a number of open research questions.