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.

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.

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.     

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.     

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.     

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.     

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 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.     

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.     

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.     

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.     

Effective message synchronization methods for multiplayer online games with maps

A solution for the message synchronization problem of the client-server based game system is to wait for a certain period of time (waiting period) until the server processes the messages from clients. However, identifying a suitable waiting period proves challenging. In this paper, we develop two methods to determine the waiting period using the probability of interaction among participants and ranking of the delay among participants. Our methods take advantage of the property of games with geographical mapping, where each participant usually interacts with other participants only in the immediate surroundings. Simulation is performed for the evaluation of the methods and the shows that the proposed methods produce better results with lower average unfair ratio while keeping the same responsiveness compared to the method with prefixed waiting period.     

Bayesian event detection for sport games with hidden Markov model

Event detection can be defined as the problem of detecting when a target event has occurred, from a given data sequence. Such an event detection problem can be found in many fields in science and engineering, such as signal processing, pattern recognition, and image processing. In recent years, many data sequences used in these fields, especially in video data analysis, tend to be high dimensional. In this paper, we propose a novel event detection method for high-dimensional data sequences in soccer video analysis. The proposed method assumes a Bayesian hidden Markov model with hyperparameter learning in addition to the parameter leaning. This is in an attempt to reduce undesired influences from ineffective components within the high-dimensional data. Implemention is performed by Markov Chain Monte Carlo. The proposed method was tested against an event detection problem with sequences of 40-dimensional feature values extracted from real professional soccer games. The algorithm appears functional.     

SLA-based operations of massively multiplayer online games in clouds

Successful massively multiplayer online games (MMOGs) have today millions of registered users and hundreds of thousands of active concurrent players. To be able to guarantee quality of service (QoS) to a highly variable number of concurrent users, game operators statically over-provision a large infrastructure capable of sustaining the game peak load, even though a large portion of the resources is unused most of the time. To address this problem, we introduce in this work a new MMOG ecosystem for hosting and provisioning of MMOGs which effectively splits the traditional monolithic MMOG companies into three main service providers: game providers, game operators, and resource providers. Their interaction is regulated through comprehensive service level agreements (SLA) that establish the price, terms of operation, and compensation for service violations. In our model, game operators efficiently provision resources for MMOGs from multiple cloud providers, based on dynamic load forecasts, and ensure proper game operation that maintains the required QoS to all clients under varying resource availability. Game providers manage multiple distributed MMOGs for which they lease services under strict operational SLAs from game operators to satisfy all client requests. These three self-standing, smaller, more agile service providers enable access to the MMOG market for the small and medium enterprises, and to the current commercial cloud providers. We evaluate, through simulations based on real-life MMOG traces and commercial cloud SLAs, the impact of resource availability on the QoS offered to the MMOG clients. We find that our model can mitigate the negative effects of resource failures within four minutes and that MMOG server consolidation can accentuate the negative effects of failures in a resource-scarce environment. We further investigate different methods of ranking MMOG operational offers with either single or multiple (competing) MMOG providers. Our results show that compensations for SLA faults in the offer selection process can lead up to 11–16 % gain in the game providers’ income. We also demonstrate that adequate ranking of offers can lead to MMOG operational cost reductions from 20 up to 60 %.     

A Bayesian approach for place recognition

This paper presents a robust place recognition algorithm for mobile robots that can be used for planning and navigation tasks. The proposed framework combines nonlinear dimensionality reduction, nonlinear regression under noise, and Bayesian learning to create consistent probabilistic representations of places from images. These generative models are incrementally learnt from very small training sets and used for multi-class place recognition. Recognition can be performed in near real-time and accounts for complexity such as changes in illumination, occlusions, blurring and moving objects. The algorithm was tested with a mobile robot in indoor and outdoor environments with sequences of 1579 and 3820 images, respectively. This framework has several potential applications such as map building, autonomous navigation, search-rescue tasks and context recognition.