Suiping Zhou

Time-space consistency in large-scale distributed virtual environments

Maintaining a consistent view of the simulated world among different simulation nodes is a fundamental problem in large-scale distributed virtual environments (DVEs). In this paper, we characterize this problem by quantifying the time-space inconsistency in a DVE. To this end, a metric is defined to measure the time-space inconsistency in a DVE. One major advantage of the metric is that it may be estimated based on some characteristic parameters of a DVE, such as clock asynchrony, message transmission delay, the accuracy of the dead reckoning algorithm, the kinetics of the moving entity, and human factors. Thus the metric can be used to evaluate the time-space consistency property of a DVE without the actual execution of the DVE application, which is especially useful in the design stage of a DVE. Our work also clearly shows how the characteristic parameters of a DVE are interrelated in deciding the time-space inconsistency, so that we may fine-tune the DVE to make it as consistent as possible. To verify the effectiveness of the metric, a Ping-Pong game is developed. Experimental results show that the metric is effective in evaluating the time-space consistency property of the game.

Crowd modeling and simulation technologies

As a collective and highly dynamic social group, the human crowd is a fascinating phenomenon that has been frequently studied by experts from various areas. Recently, computer-based modeling and simulation technologies have emerged to support investigation of the dynamics of crowds, such as a crowds behaviors under normal and emergent situations. This article assesses the major existing technologies for crowd modeling and simulation. We first propose a two-dimensional categorization mechanism to classify existing work depending on the size of crowds and the time-scale of the crowd phenomena of interest. Four evaluation criteria have also been introduced to evaluate existing crowd simulation systems from the point of view of both a modeler and an end-user. We have discussed some influential existing work in crowd modeling and simulation regarding their major features, performance as well as the technologies used in this work. We have also discussed some open problems in the area. This article will provide the researchers with useful information and insights on the state of the art of the technologies in crowd modeling and simulation as well as future research directions.

Hybrid modelling of crowd simulation

Macroscopic and microscopic modeling have become mainstream methodologies for crowd simulation in dynamic environments. The two models make a trade-off between efficiency and accuracy, but neither of them is able to achieve both goals at the same time. With the aim of achieving both efficiency and accuracy, a hybrid modelling method is proposed in this paper for crowd simulation. This paper illustrates how the two types of models co-exist in a single simulation and work collaboratively. A case study for this method is also conducted, the simulation result of which shows that the proposed method can not only benefit from the macroscopic model by improving the simulation efficiency, but also obtain a fine-grained simulation result by adopting the microscopic model.

Update Scheduling for Improving Consistency in Distributed Virtual Environments

The fundamental goal of distributed virtual environments (DVEs) is to create a common and consistent presentation of the virtual world among a set of computers interconnected by a network. This paper investigates update scheduling algorithms to make efficient use of network capacity and improve consistency in DVEs. Our approach is to schedule state updates according to their potential impacts on consistency. In DVEs, the perceptions of participants are affected by both the spatial magnitude and temporal duration of inconsistency in the virtual world. Using the metric of time-space inconsistency, we analytically derive the optimal update schedules for minimizing the impact of inconsistency. Based on the analysis, we propose a number of scheduling algorithms that integrate spatial and temporal factors. These algorithms also take into consideration the effect of network delays. The algorithms can be used on top of many existing mechanisms such as dead reckoning. Experimental results show that our proposed algorithms significantly outperform the intuitive algorithms that are based on spatial or temporal factors only.

Modeling and simulation of pedestrian behaviors in crowded places

Pedestrian simulation has many applications in computer games, military simulations, and animation systems. A realistic pedestrian simulation requires a realistic pedestrian behavioral model that takes into account the various behavioral aspects of a real pedestrian. In this article, we describe our work on such a model, which aims to generate human-like pedestrian behaviors. To this end, various important factors in a real-pedestrians decision-making process are considered in our model. These factors include a pedestrians sensory attention, memory, and navigational behaviors. In particular, a two-level navigation model is proposed to generate realistic navigational behavior. As a result, our pedestrian model is able to generate various realistic behaviors such as overtaking, waiting, side-stepping and lane-forming in a crowded area. The simulated pedestrians are also able to navigate through complex environment, given an abstract map of the environment.

Critical causal order of events in distributed virtual environments

We investigate the causal order of events in distributed virtual environments (DVEs). We first define the critical causal order relation among the events. Then, we propose some mechanisms to enhance the prevalent RO (receive order delivery) mechanism in DVEs so that the real-time property of DVEs is preserved while the critical causal order violations are reduced. These mechanisms are implemented as a middleware. Experimental results show that the middleware performs well in reducing the critical causality violations in simulation and incurs little processing overhead.

A hybrid Interest Management mechanism for peer-to-peer Networked Virtual Environments

An Interest Management (IM) mechanism eliminates irrelevant status updates transmitted in Networked Virtual Environments (NVE). However, IM itself involves both computation and communication overhead, of which the latter is the focus of this paper. Traditionally, there are area-based and cell-based IM mechanisms. This paper proposes a hybrid IM mechanism for peer-to-peer NVEs, that utilizes the cell-based mechanism to reduce Area-Of-Interest (AOI) updates in the area-based mechanism so as to reduce its communication overhead. To compare the new mechanism with the two traditional approaches, a multiplayer game scenario is simulated. The performance results show that, compared to the traditional mechanisms, the hybrid mechanism reduces the upload bandwidth consumption by more than 25.28 percent, reduces the overhead ratio from more than 67.54 percent to only 25.17 percent, and allows more than 5000 players in the Internet to join the same game with todays network upload bandwidth.

Cluster based partitioning for agent-based crowd simulations

Simulating crowds is a challenging but important problem. There are various methodologies in the literature ranging from macroscopic numerical flow simulations to detailed, microscopic agent simulations. One key issue for all crowd simulations is scalability. Some methods address this issue through abstraction, describing global properties of homogeneous crowds. However, ideally a modeler should be able to simulate large heterogeneous crowds at fine levels of detail. We are attempting to achieve scalability through the application of distributed simulation techniques to agent-based crowd simulation. Distributed simulation, however, introduces its own challenges, in particular how to efficiently partition the load between a number of machines. In this paper we introduce a method of partitioning agents onto machines using an adapted k-means clustering algorithm. We present, validate and use an analysis tool to compare the proposed clustered partitioning approach with a series of existing methods.

An Extensible Collision Avoidance Model for Realistic Self-Driven Autonomous Agents

We have developed a novel collision avoidance model and shown how the model can be used as a basic building block to generate various behaviors for intelligent agents. The main difference between our model and other path planning models is that our model is concerned with the execution of a planned path in a dynamic environment rather than the planning of the path itself. As a result, our model reflects more closely the decision process of humans in collision avoidance. We argue that collision avoidance is in fact a highly complex social interaction between two or more agents, and that our model has the potential of creating a truly heterogenous population for realistic crowd simulations. We have also conducted some basic experiments on the model to investigate the resultant behaviors. The results show that the proposed model is effective in generating various human-like collision avoidance behaviours.

Fuzzy CMAC With Incremental Bayesian Ying–Yang Learning and Dynamic Rule Construction

Inspired by the philosophy of ancient Chinese Taoism, Xus Bayesian ying-yang (BYY) learning technique performs clustering by harmonizing the training data (yang) with the solution (ying). In our previous work, the BYY learning technique was applied to a fuzzy cerebellar model articulation controller (FCMAC) to find the optimal fuzzy sets; however, this is not suitable for time series data analysis. To address this problem, we propose an incremental BYY learning technique in this paper, with the idea of sliding window and rule structure dynamic algorithms. Three contributions are made as a result of this research. First, an online expectation-maximization algorithm incorporated with the sliding window is proposed for the fuzzification phase. Second, the memory requirement is greatly reduced since the entire data set no longer needs to be obtained during the prediction process. Third, the rule structure dynamic algorithm with dynamically initializing, recruiting, and pruning rules relieves the ¿curse of dimensionality¿ problem that is inherent in the FCMAC. Because of these features, the experimental results of the benchmark data sets of currency exchange rates and Mackey-Glass show that the proposed model is more suitable for real-time streaming data analysis.