Norm negotiation in online multi-player games

In recent years, the proliferation of VOIP data has created a number of applications in which it is desirable to perform quick online classification and recognition of massive voice streams. Typically such applications are encountered in real time intelligence and surveillance. In many cases, the data streams can be in compressed format, and the rate of data processing can often run at the rate of Gigabits per second. All known techniques for speaker voice analysis require the use of an offline training phase in which the system is trained with known segments of speech. The state-of-the-art method for text-independent speaker recognition is known as Gaussian mixture modeling (GMM), and it requires an iterative expectation maximization procedure for training, which cannot be implemented in real time. In many real applications (such as surveillance) it is desirable to perform the recognition process in online time, so that the system can be quickly adapted to new segments of the data. In many cases, it may also be desirable to quickly create databases of training profiles for speakers of interest. In this paper, we discuss the details of such an online voice recognition system. For this purpose, we use our micro-clustering algorithms to design concise signatures of the target speakers. One of the surprising and insightful observations from our experiences with such a system is that while it was originally designed only for efficiency, we later discovered that it was also more accurate than the widely used GMM. This was because of the conciseness of the micro-cluster model, which made it less prone to over training. This is evidence of the fact that it is often possible to get the best of both worlds and do better than complex models both from an efficiency and accuracy perspective. We present experimental results illustrating the effectiveness and efficiency of the method.

Hack-proof synchronization protocol for multi-player online games

Synchronization protocols based on “dead-reckoning” are vulnerable to a popular type of cheat called speed-hack. A speed-hack helps a cheater to gain unfair advantages by essentially speeding up the actions of the avatar controlled by the cheater, so that the cheater can move, explore and gather items faster than honest players. This paper presents a novel version of a dead-reckoning protocol that is invulnerable to speed-hacks. Existing games based on dead-reckoning can easily be modified to use this hack-proof dead-reckoning protocol and how the protocol works on both client-server architecture and peer-to-peer (P2P) architecture will be demonstrated in this paper.

Online gaming: a scoping study of massively multi-player online role playing games

The popularity of Massively Multiplayer Online Role-Playing Games (MMORPGs) has risen greatly over the last few years. To date there has been very little published academic research concerning online gaming and even less on the different types of online games that exist. Given the lack of data, a scoping study was undertaken to examine the extent, range and nature of different MMORPGs. Data were collected relating to the twenty most popular MMORPGs. The primary aim was to present a summarised overview of all material reviewed. The secondary aim was to provide a thematic construction in order to present a narrative account of existing MMORPG literature. Overall, the scoping study found that whilst some games had received moderate, or even substantial attention, others have had no research conducted upon them at all. This presents a problem since the growth in both the player base and the industry suggests that a single psychological profile of ‘the gamer’ cannot be relied upon, and as such further research is required. It is hoped that this study suggests ways forward and helps set research agendas for future research into MMORPGs.     

Iterative ADP learning algorithms for discrete-time multi-player games

Adaptive dynamic programming (ADP) is an important branch of reinforcement learning to solve various optimal control issues. Most practical nonlinear systems are controlled by more than one controller. Each controller is a player, and to make a tradeoff between cooperation and conflict of these players can be viewed as a game. Multi-player games are divided into two main categories: zero-sum game and non-zero-sum game. To obtain the optimal control policy for each player, one needs to solve Hamilton–Jacobi–Isaacs equations for zero-sum games and a set of coupled Hamilton–Jacobi equations for non-zero-sum games. Unfortunately, these equations are generally difficult or even impossible to be solved analytically. To overcome this bottleneck, two ADP methods, including a modified gradient-descent-based online algorithm and a novel iterative offline learning approach, are proposed in this paper. Furthermore, to implement the proposed methods, we employ single-network structure, which obviously reduces computation burden compared with traditional multiple-network architecture. Simulation results demonstrate the effectiveness of our schemes.     

不对称约束多人非零和博弈的自适应评判控制

本文针对连续时间非线性系统的不对称约束多人非零和博弈问题, 建立了一种基于神经网络的自适应评判控制方法. 首先, 本文提出了一种新颖的非二次型函数来处理不对称约束问题, 并且推导出最优控制律和耦合Hamilton-Jacobi方程. 值得注意的是, 当系统状态为零时, 最优控制策略是不为零的, 这与以往不同. 然后, 通过构建单一评判网络来近似每个玩家的最优代价函数, 从而获得相关的近似最优控制策略. 同时, 在评判学习期间发展了一种新的权值更新规则. 此外, 通过利用Lyapunov理论证明了评判网络权值近似误差和闭环系统状态的稳定性. 最后, 仿真结果验证了本文所提方法的有效性     

UCT-RAVE算法在多人非完备信息博弈中的应用

针对传统博弈搜索算法无法适用于多人非完备信息博弈,通过分析UCT-RAVE算法的原理和特性,提出了运用UCT-RAVE算法与蒙特卡罗抽样技术相结合的方法.通过蒙特卡罗抽样技术将非完备信息提取为有一定可信度的完备信息,运用UCT-RAVE算法基于此完备信息进行搜索,结合多次蒙特卡罗抽样下的最佳收益,选择最适行动.实例结果表明了该方法的可行性和有效性.     

Problematic involvement in online games: A cluster analytic approach

Playing online games can become problematic and engender adverse consequences. Several psychological factors have been shown to influence the development and the maintenance of this problematic behavior, including impulsivity traits, motives to play (immersion, achievement, social affiliation), and self-esteem. The aim of the current study is to determine whether reliable subtypes of problematic online gamers can be identified. A sample of 1057 online gamers was recruited. Validated questionnaires were used to measure established psychological risk factors (impulsivity, motives to play, self-esteem) and potential consequences of playing (addiction symptoms, positive and negative affect). Actual in-game behaviors were also monitored. Five reliable clusters of gamers were identified (three problematic and two nonproblematic clusters). Cluster comparison revealed that the psychological factors considered are differentially involved in problematic online gaming. At the theoretical level, the results emphasized that problem online gaming depends on a wide range of psychological factors. At the clinical level, the diversity of psychological profiles shown supports the development of personalized (custom-made) interventions targeting specific psychological mechanisms. Overall, our findings suggest that conceptualizing the problematic use of massively multiplayer online role-playing games as “behavioral addiction” is too restrictive and might result in the simplification of heterogeneous and multi-determined problematic behaviors.     

Novel threat-based AI strategies that incorporate adaptive data structures for multi-player board games

This paper considers the problem of designing novel techniques for multi-player game playing, in a range of board games and configurations. Compared to the well-known case of two-player game playing, multi-player game playing is a more complex problem with unique requirements. To address the unique challenges of this domain, we examine the potential of employing techniques inspired by Adaptive Data Structures (ADSs) to rank opponents based on their relative threats, and using this information to achieve gains in move ordering and tree pruning. We name our new technique the Threat-ADS heuristic. We examine the Threat-ADS’ performance within a range of game models, employing a number of different, well-understood update mechanisms for ADSs. We then extend our analysis to specifically consider intermediate board states, which are more interesting than the initial board state, as we do not assume the availability of “Opening book” moves, and where substantial variation can exist, in terms of available moves and threatening opponents. We expand this analysis to include an exploration of the Threat-ADS heuristic’s performance in deeper ply game trees, to confirm that it maintains its benefits even when lookahead is greater, and with an expanded examination of how the number of players present in the game impacts the performance of the Threat-ADS heuristic. We find that in nearly all environments, the Threat-ADS heuristic is able to produce meaningful, statistically significant improvements in tree pruning, demonstrating that it serves as a very reliable move ordering heuristic for multi-player game playing under a wide range of configurations, thus motivating the use of ADS-based techniques within the field of game playing.     

Commitment to multiplayer online games: An investment model approach

Multiplayer online games have highly committed players who continue playing these games for years. In the present study, commitment to multiplayer online games was examined from an investment model perspective. The investment model was originally developed to explain persistence in romantic relationships, ultimately identifying the three predictors of commitment as satisfaction, quality of alternatives, and investment size. Furthermore, commitment has been linked to greater accommodation (i.e., constructive responses) in relationship conflicts. The present study extended the model to the context of multiplayer online games. Participants (N = 176) who regularly played a multiplayer online game completed modified versions of the investment model and accommodation scales in an online questionnaire. Structural equation modeling analyses showed that satisfaction and investment predicted greater game commitment, which in turn predicted greater accommodation toward the game's problems (i.e., lower endorsement of exit behaviors, greater endorsement of voice and loyalty behaviors). Findings showed that the investment model provides a valid framework for examining game commitment.     

一种内存错误的动态检测方法*

提出了一种内存错误的动态检测方法,通过统一的内存错误检测模型和接口,使内存错误检测处理过程规范化,便于扩展。实验表明,该方法可以方便地进行扩展,以增加内存错误的检测能力。     

A probabilistic approach to quantum Bayesian games of incomplete information

A Bayesian game is a game of incomplete information in which the rules of the game are not fully known to all players. We consider the Bayesian game of Battle of Sexes that has several Bayesian Nash equilibria and investigate its outcome when the underlying probability set is obtained from generalized Einstein–Podolsky–Rosen experiments. We find that this probability set, which may become non-factorizable, results in a unique Bayesian Nash equilibrium of the game.     

Dynamic competition in IT security: A differential games approach

Hackers evaluate potential targets to identify poorly defended firms to attack, creating competition in IT security between firms that possess similar information assets. We utilize a differential game framework to analyze the continuous time IT security investment decisions of firms in such a target group. We derive the steady state equilibrium of the duopolistic differential game, show how implicit competition induces overspending in IT defense, and then demonstrate how such overinvestment can be combated by innovatively managing the otherwise misaligned incentives for coordination. We show that in order to achieve cooperation, the firm with the higher asset value must take the lead and provide appropriate incentives to elicit participation of the other firm. Our analysis indicates that IT security planning should not remain an internal, firm-level decision, but also incorporate the actions of those firms that hackers consider as alternative targets.     

Practical middleware for massively multiplayer online games

A massively multiplayer online game (MMOG) lets thousands of players interact simultaneously within a virtual world via the Internet. Middleware plays an important role in the development of next-generation MMOGs, which must be built on platforms that address not only the service aspect, but also code maintainability and development for programmers. The authors' compact, high-performance message-oriented middleware has a code-generation programming model that is designed to address many of these problems.     

Dynamic pricing of call rates: Bayesian approach

In this paper, we present different cases and their possible solutions in the telecommunications market by incorporating dynamically changing call rates over the channel depending upon the network congestion. Since dynamic pricing of call rates is beneficial from both the perspectives of subscribers and service providers, our solution can significantly help to adapt this pricing mechanism in real market scenario. In order to deploy this scheme, we have incorporated the competing network provider's strategy into the mechanism of deciding dynamic price. Establishment of Nash equilibrium with the competing network provider has stabilized our pricing mechanism.     

LED芯片在线检测方法研究

对于封装过程中的LED芯片的检测目前还没有行之有效的方法,基于p-n结的光生伏特效应和法拉第定律,提出一种非接触式的针对LED封装过程中芯片质量及芯片与支架之间连接状态的检测方法。根据实际LED芯片所处的闭合短路状态,感应回路由绕在高磁导率条形磁芯上的多匝线圈构成。磁芯采用不同搭接方式以提高检测的信噪比,根据实验结果确定了磁芯的最佳搭接方式。实验结果表明,该方法具有较高的检测精度,可以实现对闭合短路状态微安量级光生电流的检测。计算结果与实验结果吻合较好。     

Understanding customer regional differences from online opinions: a hierarchical Bayesian approach

Electronic Commerce Research - A large volume of customer reviews is generated from time to time and customer requirements are presented between lines of online opinions. Many studies about online...     

A load balancing scheme for massively multiplayer online games

In a distributed MMOG (massively multiplayer online game) server architecture, the server nodes may become easily overloaded by the high demand from the players for state updates. Many works propose algorithms to distribute the load on the server nodes, but this load is usually defined as the number of players on each server, what is not an ideal measure. Also, the possible heterogeneity of the system is frequently overlooked. We propose a balancing scheme with two main goals: allocate load on server nodes proportionally to each one’s power and reduce the inter-server communication overhead, considering the load as the occupied bandwidth of each server. Four algorithms were proposed, from which ProGReGA is the best for overhead reduction and ProGReGA-KF is the most suited for reducing player migrations between servers. We also make a review of related works and some comparisons were made, where our approach performed better.     

基于贝叶斯−斯坦科尔伯格博弈的SDN安全控制平面模型

提出一种基于动态异构冗余的安全控制平面,通过动态地变换异构的控制器以增加攻击者的难度。首先,提出基于贝叶斯?斯坦科尔伯格博弈模型的控制器动态调度方法,将攻击者和防御者作为博弈参与双方,求得均衡解,进而指导调度策略;其次,引入一种自清洗机制,与博弈策略结合形成闭环的防御机制,进一步地提高了控制层的安全增益;最后,实验定量地描述了基于该博弈策略的安全控制层相比与传统部署单个控制器以及采用随机策略调度控制器的收益增益,并且自清洗机制能够使控制平面一直处于较高的安全水平。     

A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot

We address the problem of online path planning for optimal sensing with a mobile robot. The objective of the robot is to learn the most about its pose and the environment given time constraints. We use a POMDP with a utility function that depends on the belief state to model the finite horizon planning problem. We replan as the robot progresses throughout the environment. The POMDP is high-dimensional, continuous, non-differentiable, nonlinear, non-Gaussian and must be solved in real-time. Most existing techniques for stochastic planning and reinforcement learning are therefore inapplicable. To solve this extremely complex problem, we propose a Bayesian optimization method that dynamically trades off exploration (minimizing uncertainty in unknown parts of the policy space) and exploitation (capitalizing on the current best solution). We demonstrate our approach with a visually-guide mobile robot. The solution proposed here is also applicable to other closely-related domains, including active vision, sequential experimental design, dynamic sensing and calibration with mobile sensors.