Linbo Luo

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.

Density-based evolutionary framework for crowd model calibration

Crowd modeling and simulation is an important and active research field, with a wide range of applications such as computer games, military training and evacuation modeling. One important issue in crowd modeling is model calibration through parameter tuning, so as to produce desired crowd behaviors. Common methods such as trial-and-error are time consuming and tedious. This paper proposes an evolutionary framework to automate the crowd model calibration process. In the proposed framework, a density-based matching scheme is introduced. By using the dynamic density of the crowd over time, and a weight landscape to emphasize important spatial regions, the proposed matching scheme provides a generally applicable way to evaluate the simulated crowd behaviors. Besides, a hybrid search mechanism based on differential evolution is proposed to efficiently tune parameters of crowd models. Simulation results demonstrate that the proposed framework is effective and efficient to calibrate the crowd models in order to produce desired macroscopic crowd behaviors.

A Computational Model of Emotions for Agent-Based Crowds in Serious Games

Crowd behaviour is an interesting social phenomenon that emerges from complex interactions of individuals. An important aspect of individual behaviour is emotion which plays a significant role in all aspects of human decision making. For example, heightened emotional states can lead people to take highly unexpected or irrational actions. One popular motivation for simulation of virtual crowds is to generate believable characters in movies and computer games. Recently the concept of serious games has been introduced in both academic and industrial circles. In this paper, we propose an emotion engine, based on modern appraisal theory, that is able to model various emotional crowd characteristics. This appraisal engine is capable of capturing the dynamics of emotional contagion and we show how different crowd composition can lead to different patterns of emotional contagion. In addition, we describe a serious game designed for training military personnel in peaceful crowd control. We evaluate this engine in the context of a property protection protest scenario where the players or soldiers are tasked to maintain a peaceful protest without violence. A systematic evaluation is presented which supports the facial validity of the emotion engine and our model of emotional contagion.

Toward a Generic Framework for Modeling Human Behaviors in Crowd Simulation

This paper presents our ongoing work on modeling agents with human-like decision making and behavior execution capabilities in crowd simulation. We aim to provide a generic framework that reflects the major cognitive and physical processes as observed from human behaviors in real-life situations. The design of the framework is based on some basic assumptions and related cognitive theories on human behaviors in various real-life situations. In this paper, the cognitive architecture of our framework is presented, which emphasizes the role of experiences in humans decision making. The paper also briefly describes the design of agents decision making process and presents a case study to show some results in a crowd simulation scenario.

Ea-based evacuation planning using agent-based crowd simulation

Safety planning for crowd evacuation is an important and active research topic nowadays. One important issue is to devise the evacuation plans of individuals in emergency situations so as to reduce the total evacuation time. This paper proposes a novel evolutionary algorithm (EA)-based methodology, together with agent-based crowd simulation, to solve the evacuation planning problem. The proposed method features a novel segmentation strategy which divides the entire evacuation region into sub-regions based on a discriminant function. Each sub-region is assigned with an exit gate, and individuals in a sub-region will run toward the corresponding exit gate for evacuation. In this way, the evacuation planning problem is converted to a symbolic regression problem. Then an evolutionary algorithm, using agent-based crowd simulation as fitness function, is developed to search for the global optimal solution. The simulation results on different scenarios demonstrate that the proposed method is effective to reduce the evacuation time.

Interactive scenario generation for mission‐based virtual training

For a virtual training system, how to effectively and quickly generate training scenarios has become a challenging issue. A scenario generation system is needed to produce scenarios that can meet different objectives and at the same time be customized for individuals. In this paper, we introduce a scenario generation framework for mission‐based virtual training, which aims to generate scenarios from both trainer and trainees perspective. The framework allows a trainer to direct the scenario generation process, so that the generated scenarios reflect the trainers preferences over different mission objectives. It also considers how the scenarios could adapt to different trainees’ skill levels. The representation of scenario beat is proposed, and the scenario generation process adopts a combinatorial optimization approach generating the sequence of scenario beats. The efficacy of the proposed framework is demonstrated through an empirical study of human players in a simple food distribution mission game. The results show that a trainee can achieve better performance improvement when playing the customized scenarios tailored to the trainees skill level as compared with the uncustomized scenarios. Copyright

Automatic model construction for the behavior of human crowds

Graphical abstractDisplay Omitted HighlightsWe propose an automatic methodology to learn generic crowd behavior rules from video data.We formulate the problem of finding behavioral rule from video data as a symbolic regression problem..The behavior rules learned by the proposed method are applicable to different scenarios that have similar behavior patterns.We apply the proposed method to real world datasets and the results have demonstrated the effectiveness of the proposed method. Designing suitable behavioral rules of agents so as to generate realistic behaviors is a fundamental and challenging task in many forms of computational modeling. This paper proposes a novel methodology to automatically generate a descriptive model, in the form of behavioral rules, from video data of human crowds. In the proposed methodology, the problem of modeling crowd behaviors is formulated as a symbolic regression problem and the self-learning gene expression programming is utilized to solve the problem and automatically obtain behavioral rules that match data. To evaluate its effectiveness, we apply the proposed method to generate a model from a video dataset in Switzerland and then test the generality of the model by validating against video data from the United States. The results demonstrate that, based on the observed movement of people in one scenario, the proposed methodology can automatically construct a general model capable of describing the crowd dynamics of another scenario in a different context (e.g., Switzerland vs. U.S.) as long as that the crowd behavior patterns are similar.

A review of interactive narrative systems and technologies: a training perspective

As an emerging form of digital entertainment, the interactive narrative has attracted great attention from researchers over the past decade. Recently, there has been an emerging trend to apply interactive narratives to training and simulation. An interactive narrative system allows players to proactively interact with simulated entities in a virtual world and have the ability to alter the progression of a storyline. In simulation-based training, the use of an interactive narrative system enables the possibility to offer engaging, diverse and personalized narratives or scenarios for different training purposes. This paper provides a review of interactive narrative systems and technologies from a training perspective. Specifically, we first propose a set of key requirements in developing interactive narrative systems for simulation-based training. Then we review nine representative existing systems with respect to their system architectures, features and related mechanisms. To examine their applicability to training, we investigate and compare the reviewed systems based on the functionalities and modules that support the proposed requirements. Furthermore, we discuss some open research issues on the future development of interactive narrative technologies for training applications.

Towards a data-driven approach to scenario generation for serious games

Serious games have recently shown great potential to be adopted in many applications, such as training and education. However, one critical challenge in developing serious games is the authoring of a large set of scenarios for different training objectives. In this paper, we propose a data‐driven approach to automatically generate scenarios for serious games. Compared with other scenario generation methods, our approach leverages on the simulated player performance data to construct the scenario evaluation function for scenario generation. To collect the player performance data, an artificial intelligence (AI) player model is designed to imitate how a human player behaves when playing scenarios. The AI players are used to replace human players for data collection. The experiment results show that our data‐driven approach provides good prediction accuracy on scenarios training intensities. It also outperforms our previous heuristic‐based approach in its capability of generating scenarios that match closer to specified target player performance.Copyright

Learning behavior patterns from video for agent-based crowd modeling and simulation

This paper proposes a novel data-driven modeling framework to construct agent-based crowd model based on real-world video data. The constructed crowd model can generate crowd behaviors that match those observed in the video and can be used to predict trajectories of pedestrians in the same scenario. In the proposed framework, a dual-layer architecture is proposed to model crowd behaviors. The bottom layer models the microscopic collision avoidance behaviors, while the top layer models the macroscopic crowd behaviors such as the goal selection patterns and the path navigation patterns. An automatic learning algorithm is proposed to learn behavior patterns from video data. The learned behavior patterns are then integrated into the dual-layer architecture to generate realistic crowd behaviors. To validate its effectiveness, the proposed framework is applied to two different real world scenarios. The simulation results demonstrate that the proposed framework can generate crowd behaviors similar to those observed in the videos in terms of crowd density distribution. In addition, the proposed framework can also offer promising performance on predicting the trajectories of pedestrians.