The effects of a player’s network centrality on resource accessibility,game enjoyment,and continuance intention: A study on online gaming communities

This study applies social capital theory to investigate how a player’s network centrality in an online gaming community (i.e., a guild) affects his/her attitude and continuance intention toward a Massive Multiplayer Online Game (MMOG). Analysis of 347 usable responses shows that players’ network centrality has a negative impact on their ties to players who belong to other guilds (i.e., non-guild interaction), but a positive effect on players’ access to resources. However, players’ network centrality fails to increase their perceived game enjoyment directly. Players’ resource accessibility and perceived game enjoyment play mediating roles in the relationship between network centrality and attitude toward playing an MMOG, which in turn influences game continuance intention. The results also show that although players’ non-guild interaction is negatively related to their resource accessibility from the networks, it is positively associated with perceived game enjoyment. The article concludes with implications and limitations of the study.     

Game-theoretic Evolutionary Algorithm Based on Behavioral Expectation and its Performance Analysis

Game theory has been widely recognized as an important tool in many fields, which provides general mathematical techniques for analyzing situations in which two or more individuals make decisions that will influence one another’s welfare. This paper presents a game-theoretic evolutionary algorithm based on behavioral expectation, which is a type of optimization approach based on game theory. A formulation to estimate the payoffs expectation is given, which is a mechanism of trying to master the player’s information so as to facilitate the player becoming the rational decision maker. GameEA has one population (players set), and generates new offspring only by the imitation operator and the belief learning operator. The imitation operator is used to learn strategies and actions from other players to improve its competitiveness and applies it into the future game, namely that one player updates its chromosome by strategically copying some segments of gene sequences from the competitor. Belief learning refers to models in which a player adjusts its own strategies, behavior or chromosome by analyzing current history information with respect to an improvement of solution quality. The experimental results on various classes of problems using real-valued representation show that GameEA outperforms not only the standard genetic algorithm (GA) but also other GAs having additional mechanisms of accuracy enhancement. Finally, we compare the convergence of GameEA with different numbers of players to determine whether this parameter has a significant effect on convergence. The statistical results show that at the 0.05 significance level, the number of players has a crucial impact on GameEA's performance. The results suggest that 50 or 100 players will provide good results with unimodal functions, while 200 players will provide good results for multimodal functions.     

A game attention model for efficient bit rate allocation in cloud gaming

The widespread availability of broadband internet access and the growth in server-based processing have provided an opportunity to run games away from the player into the cloud and offer a new promising service known as cloud gaming. The concept of cloud gaming is to render a game in the cloud and stream the resulting game scenes to the player as a video sequence over a broadband connection. To meet the stringent network bandwidth requirements of cloud gaming and support more players, efficient bit rate reduction techniques are needed. In this paper, we introduce the concept of game attention model (GAM), which is basically a game context-based visual attention model, as a means for reducing the bit rate of the streaming video more efficiently. GAM estimates the importance of each macro-block in a game frame from the player’s perspective and allows encoding the less important macro-blocks with lower bit rate. We have evaluated nine game video sequences, covering a wide range of game genre and a spectrum of scene content in terms of details, motion and brightness. Our subjective assessment shows that by integrating this model into the cloud gaming framework, it is possible to decrease the required bit rate by nearly 25 % on average, while maintaining a relatively high user quality of experience. This clearly enables players with limited communication resources to benefit from cloud gaming with acceptable quality.     

Are two better than one? Playing singly,playing in dyads in a computerized simulation of the Israeli–Palestinian conflict

The study examined a computerized simulation of the Israeli–Palestinian conflict as a peace education tool for Israeli female undergraduate students of Palestinian and Jewish origin. To win the game, players are required to arrive at a two-state solution of the conflict. 100 Israeli female students of Palestinian and Jewish origin either supporting or opposing this solution played the game singly or in dyads. The rules of the study required players in dyads to reach mutual agreement on every action in the game. Results indicated that 19 out of 25 dyads resolved the conflict in the game as opposed to 16 out of 50 single players. In addition, players in dyads took more political actions and less security actions than single players did. Finally, players in dyads preferred to focus more on political actions than on security actions in the game. The abovementioned results point out three key implications for peace education. First, PeaceMaker reduces the tension and the charged atmosphere between Jewish and Palestinian players. Second, the game should be played in dyads and not singly. Finally, dyads should be comprised of a Jewish player and a Palestinian player in order to challenge their interaction and game behavior.     

A model and software architecture for MMORPG traffic generation based on player behavior

In this paper we present a software architecture for traffic generation based on application level player behavior in Massively Multiplayer Online Role-Playing Games (MMORPGs). We have performed measurements of network traffic for each of the previously defined action categories for MMORPGs (Trading, Questing, Dungeons, Raiding, Player versus Player Combat, and Uncategorized), as well as measurements of application level player behavior in terms of listed action categories. Based on the obtained datasets we have created network traffic models for each action category and player behavior models with focus on hourly and daily trends. Network traffic models are implemented in Distributed Internet Traffic Generator and verified through comparison with the real traffic. Player behavior models explore number of active players, session duration, as well as lengths and probability of session segments (i.e., parts of the session consisting of only one category of player actions). We propose an architecture which enables scalable, behavior-driven traffic generation and implement it in a laboratory testbed. In order to achieve scalability of the system, we use Linux Containers as a virtualization technique. The resulting implementation can generate hundreds of MMORPG streams on a single PC. As a case study we used Activision Blizzard’s World of Warcraft.     

Multi-objective design method based on evolution game and its application for suspension

Through research and bionics of biology survival mode, game players with competition, cooperation and self-adaptation capacity are introduced in the multi-objective design. The dynamic behavior and bounded rationality in game processes for players are considered according to Chinese saying “In success, commit oneself to the welfare of the society; in distress, maintain one’s own integrity”. An evolution rule, Poor-Competition-Rich-Cooperation (short for PCRC), is proposed. Then, the corresponding payoff functions of competition and cooperation behavior are established and a multi-objective design method based on evolution game is proposed. The calculation steps are as follows: 1) Taking the design objectives as different game players, and calculating factors of the design variables to objective and fuzzy clustering. The design variables are divided into multiple strategy subsets owned by each game player. 2) According to the evolution rule, each player determines its behavior and payoff function in this game round. 3) In their own strategy subsets, each game player takes their payoff as mono-objective for optimization. It gives the best strategy upon other players. And so the best strategies of all players conform the group strategy in this round. The final equilibrium solution is obtained through multi-round game based on convergence criterion. The validity and reliability of this method are shown by the results of an example of a tri-objective optimization design of passive suspension parameters.     

On frame rate and player performance in first person shooter games

The rate at which frames are rendered in a computer game directly impacts player performance, influencing both the game playability and enjoyability. However, despite the importance of frame rate and the wide-spread popularity of computer games, to the best of our knowledge, there is little quantitative understanding of the effects of frame rate on player performance in computer games. This paper provides a unique classification of actions in First Person Shooter (FPS) games based on interaction requirements that allow qualitative assessment of the impact of frame rates on player performance. This qualitative assessment is supported by quantitative analysis from two large user studies that measure the effects of frame rate on the fundamental player actions in a FPS game. Nearly 100 users participated in the two user study experiments, providing performance and perception data over a range of frame rates commonly studied for video streaming and inclusive of frame rates found in many computer game platforms. In general, the analysis shows that actions that require precise, rapid response, such as shooting, are greatly impacted by degradations in frame rates, while actions with lower precision and response requirements, such as moving, are more tolerant of low frame rates. These insights into the effects of frame rates on player performance can guide players in their choice for game settings and new hardware purchases, and inform system designers in their development of new hardware.     

Blowtooth: a provocative pervasive game for smuggling virtual drugs through real airport security

In this paper, we describe a pervasive game, Blowtooth, in which players use their mobile phones to hide virtual drugs on nearby airline passengers in real airport check-in queues. After passing through airport security, the player must find and recover their drugs from the innocent bystanders, without them ever realising they were involved in the game. The game explores the nature of pervasive game playing in environments that are not, generally, regarded as playful or “fun”. This paper describes the game’s design and implementation as well as an evaluation conducted with participants in real airports. It explores the players’ reactions to the game through questionnaire responses and in-game activity. The technologies used in Blowtooth are, intentionally, simple in order for the enjoyment of the game to be reliant more on the physical environment rather than the enabling technologies. We conclude that situating pervasive games in unexpected and challenging environments, such as international airports, may provide interesting and unique gaming experiences for players. In addition, we argue that pervasive games benefit most from using the specific features and nature of interesting real-world environments rather than focusing on the enabling technologies.     

Behavlets: a method for practical player modelling using psychology-based player traits and domain specific features

As player demographics broaden it has become important to understand variation in player types. Improved player models can help game designers create games that accommodate a range of playing styles, and may also facilitate the design of systems that detect the currently-expressed player type and adapt dynamically in real-time. Existing approaches can model players, but most focus on tracking and classifying behaviour based on simple functional metrics such as deaths, specific choices, player avatar attributes, and completion times. We describe a novel approach which seeks to leverage expert domain knowledge using a theoretical framework linking behaviour and game design patterns. The aim is to derive features of play from sequences of actions which are intrinsically informative about behaviour—which, because they are directly interpretable with respect to psychological theory of behaviour, we name ‘Behavlets’. We present the theoretical underpinning of this approach from research areas including psychology, temperament theory, player modelling, and game composition. The Behavlet creation process is described in detail; illustrated using a clone of the well-known game Pac-Man, with data gathered from 100 participants. A workshop-based evaluation study is also presented, where nine game design expert participants were briefed on the Behavlet concepts and requisite models, and then attempted to apply the method to games of the well-known first/third-person shooter genres, exemplified by ‘Gears of War’, (Microsoft). The participants found 139 Behavlet concepts mapping from behavioural preferences of the temperament types, to design patterns of the shooter genre games. We conclude that the Behavlet approach has significant promise, is complementary to existing methods and can improve theoretical validity of player models.     

Improving player experience in Computer Games by using players' behavior analysis and linguistic descriptions

Understanding player behavior has an interest to computer games researchers and developers since it allows them to improve the design and implementation of computer games and also to ensure that players have the expected experiences. Currently this knowledge is not usually reported to players as feedback, although sometimes it is already used as an analysis tool. This paper presents a novel technology for automatically generating linguistic reports and immediate feedback from actions performed by players during play sessions. These reports allow developers to provide players with a more complete and personalized feedback about their behaviors, abilities, attitudes, skills or movements. In order to show and explore the possibilities of this new technology, we have incorporated it in the core of a computer game. We have evaluated positively that the incorporation of this kind of feedback into the core of YADY computer game allows us to improve the overall player experience.     

Personalise your massively multiplayer online game (MMOG) with Artemis

Better mobile computing, broadband and devices have contributed to the fast growth and popularity of massively multiplayer online games (MMOGs). Players are now expecting a more personalised gaming experience as personalisation has begun to filter into most games and not just MMOGs. In this paper, we explain how players of MMOGs can enjoy a ubiquitous and personalised gaming experience anywhere, on any device, and on any network with Artemis. MPEG-7 is not traditionally used for modelling games; however, Artemis adopts MPEG-7 for modelling the player, their device and the game, in conjunction with genetic algorithms for personalising the game, where possible.     

P2P MMOG中基于状态差的欺骗检测机制

提出一种适合P2P MMOG的基于状态差的欺骗检测机制。采用定积分对某玩家在其他玩家游戏视图中的状态差进行量化，并在参考网络延迟的基础上根据状态差判定该玩家在游戏过程中是否进行了欺骗。判断标准为既定阈值。实验结果表明，该文提出的欺骗检测机制能有效检测出导致玩家状态不一致类型的欺骗，提高了游戏的安全性。     

Match+Guardian: a secure peer-to-peer trading card game protocol

In trading card games (TCGs), players create a deck of cards from a subset of all cards in the game to compete with other players. While online TCGs currently exist, these typically rely on a client/server architecture and require clients to be connected to the server at all times. Instead, we propose, analyze and evaluate Match+Guardian (M+G), our secure peer-to-peer protocol for implementing online TCGs. We break down actions common to all TCGs and explain how they can be executed between two players without the need for a third party referee (which usually requires an unbiased server). In each action, the player is either prevented from cheating or if they do cheat, the opponent will be able to prove they have done so. We show these methods are secure and may be shuffled into new styles of TCGs. We then measure moves in a real trading card game to compare to our implementation of M+G and conclude with an evaluation of its performance on the Android^TM platform, demonstrating that M+G can be used in a peer-to-peer fashion on mobile devices.     

时间尺度与选择倾向性协同作用下的演化博弈模型

针对合作行为的涌现与维持问题，基于演化博弈理论和网络理论，提出了一种促进合作的演化博弈模型。该模型同时将时间尺度、选择倾向性引入到演化博弈中。在初始化阶段，根据持有策略的时间尺度将个体分为两种类型：一种个体在每个时间步都进行策略更新；另一种个体在每一轮博弈后，以某种概率来决定是否进行策略更新。在策略更新阶段，模型用个体对周围邻居的贡献来表征他的声誉，并假设参与博弈的个体倾向于学习具有较好声誉邻居的策略。仿真实验结果表明，所提出的时间尺度与选择倾向性协同作用下的演化博弈模型中，合作行为能够在群体中维持；惰性个体的存在不利于合作的涌现，但是个体的非理性行为反而能够促进合作。     

The Trojan Player Typology: A cross-genre,cross-cultural,behaviorally validated scale of video game play motivations

While many video game researchers have built scales to tackle the motivations that people have for playing video games, these scales are often limited by their focus on specific game genres or player cultures as well as their lack of behavioral validation. The present research offers a new scale for player motivations and then examines its validity across two distinct gaming genres and cultures, drawing from server-side data combined with survey data of 18,627 players of the Multiplayer Online Battle Arena League of Legends and 18,819 players of the Chinese Massively Multiplayer Online Game Chevalier’s Romance Online 3. Six types of player motivations were found: socializer, completionist, competitor, escapist, story-driven, and smarty-pants. Consistent with previous research on player motivations, this typology offers new insights into why people play video games and how player motivations can be used to infer players’ in-game behaviors.     

Road Pricing Design Based on Game Theory and Multi-agent Consensus

Consensus theory and noncooperative game theory respectively deal with cooperative and noncooperative interactions among multiple players/agents. They provide a natural framework for road pricing design, since each motorist may myopically optimize his or her own utility as a function of road price and collectively communicate with his or her friends and neighbors on traffic situation at the same time. This paper considers the road pricing design by using game theory and consensus theory. For the case where a system supervisor broadcasts information on the overall system to each agent, we present a variant of standard fictitious play called average strategy fictitious play (ASFP) for large-scale repeated congestion games. Only a weighted running average of all other players' actions is assumed to be available to each player. The ASFP reduces the burden of both information gathering and information processing for each player. Compared to the joint strategy fictitious play (JSFP) studied in the literature, the updating process of utility functions for each player is avoided. We prove that there exists at least one pure strategy Nash equilibrium for the congestion game under investigation, and the players' actions generated by the ASFP with inertia (players' reluctance to change their previous actions) converge to a Nash equilibrium almost surely. For the case without broadcasting, a consensus protocol is introduced for individual agents to estimate the percentage of players choosing each resource, and the convergence property of players' action profile is still ensured. The results are applied to road pricing design to achieve socially local optimal trip timing. Simulation results are provided based on the real traffic data for the Singapore case study.      

RTS游戏中用户行为的神经网络预测模型

即时战略游戏(简称RTS游戏)中,用户的行为由于游戏自身庞大的决策空间而难以预测.针对这个问题,提出了通过对RTS游戏的对战记录进行分析,建立5种结构的神经网络模型来预测用户行为的方法.模型考虑了不同时间片的状态对于决策行为的影响,设计了单时间片输入和双时间片输入的神经网络,并与基于动态贝叶斯网络的模型进行了比较.实验结果表明,基于单时间片输入的神经网络模型能够更加快速地完成训练过程并达到满意的预测准确度.     

Interpersonal influence on online game choices

Several theories stress the importance of interpersonal influence on an individual’s adoption of a product or service. However, there has been little research that empirically examined how online friends influence an individual’s online product choices. This study examines the effect of a game player’s online friends who adopted a game earlier than the player on the likelihood that the player adopts the game. Two main factors considered in this study are: (1) the number of online friends who adopted a game earlier and (2) the strength of ties between the player and the player’s online friends who adopted the game earlier. Using a hazard model with data on 1,668 game players’ gaming activities and relational connections, we find (1) the likelihood that a player adopts a particular game increases the more her online friends adopted the game earlier, and (2) the influence of the prior adopter friends on the likelihood that a player adopts the game varies with the strength of ties between the player and her prior adopter friends. But the p-values of the coefficients for the corresponding independent variables are larger than the conventional cutoff point, 0.05. Possible causes for this statistical insignificance are discussed in the text.     

Altruistic behavior in a nonantagonistic positional differential game

We consider a two-person nonantagonistic positional differential game (NPDG) whose dynamics is described by an ordinary nonlinear vector differential equation. Constraints on values of players’ controls are geometric. Final time of the game is fixed. Payoff functionals of both players are terminal. The formalization of positional strategies in an NPDG is based on the formalization and results of the general theory of antagonistic positional differential games (APDGs) (see monographs by N.N. Krasovskii and A.I. Subbotin [3, 4]). Additionally, in the present paper we assume that each player, together with the usual, normal (nor), type of behavior aimed at maximizing his own functional, can use other behavior types introduced in [2, 5]. In particular, these may be altruistic (alt), aggressive (agg), and paradoxical (par) types. It is assumed that in the course of the game players can switch their behavior from one type to another. Using the possibility of such switches in a repeated bimatrix 2 × 2 game in [5, 6] allowed to obtain new solutions of this game. In the present paper, extension of this approach to NPDGs leads to a new formulation of the problem. In particular, of interest is the question of how players’ outcomes at Nash solutions are transformed. An urgent problem is minimizing the time of “abnormal” behavior while achieving a good result. The paper proposes a formalization of an NPDG with behavior types (NPDGwBT). It is assumed that in an NPDGwBT each player, simultaneously with choosing a positional strategy, chooses also his own indicator function defined on the whole game horizon and taking values in the set {normal, altruistic, aggressive, paradoxical}. The indicator function of a player shows the dynamics of changes in the behavior type demonstrated by the player. Thus, in this NPDGwBT each player controls the choice of a pair {positional strategy, indicator function}. We define the notion of a BT-solution of such a game. It is expected that using behavior types in the NPDGwBT which differ from the normal one (so-called abnormal types) in some cases may lead to more favorable outcomes for the players than in the NPDG. We consider two examples of an NPDGwBT with simple dynamics in the plane in each of which one player keeps to altruistic behavior type over some time period. It is shown that in the first example payoffs of both players increase on a BT-solution as compared to the game with the normal behavior type, and in the second example, the sum of players’ payoffs is increased.     

Multistage network games with perfect information

We consider multistage network games with perfect information. At each time instant, a current network structure connecting the players is specified. It is assumed that each edge has some utility value (player’s benefit from being linked to another player), and players can change the network structure at each stage. We propose a method for finding optimal behavior for players in games of this type.