An information theory based behavioral model for agent-based crowd simulations

Crowds must be simulated believable in terms of their appearance and behavior to improve a virtual environment’s realism. Due to the complex nature of human behavior, realistic behavior of agents in crowd simulations is still a challenging problem. In this paper, we propose a novel behavioral model which builds analytical maps to control agents’ behavior adaptively with agent-crowd interaction formulations. We introduce information theoretical concepts to construct analytical maps automatically. Our model can be integrated into crowd simulators and enhance their behavioral complexity. We made comparative analyses of the presented behavior model with measured crowd data and two agent-based crowd simulators

An information theoretical approach to crowd simulation

Crowd constitutes a critical component in many virtual environment and entertainment applications. In this thesis, we propose methods to solve two distinct problems in crowd simulation domain; automatic camera control and adaptive behavioral modeling. As the basis of our methods, we develop a framework which uses information theoretical concepts to automatically construct analytical maps of crowd's locomotion, which are called behavior maps. The developed framework contains a probabilistic model of the scene to build behavior maps. In the first part of this thesis, we propose a novel automatic camera control technique which utilizes behavior maps to find interest points which represent either characteristic behaviors of the crowd or novel events occurring in the scene. The camera is updated accordingly to display selected interest points. In the second part of this thesis, we propose a novel behavioral model which uses behavior maps to control agents' behavior adaptively with agent-crowd interaction formulations. Our model can be integrated into crowd simulators and enhance their behavioral complexity. We made comparative analyses of the presented behavior model with measured crowd data and two agent-based crowd simulators

Designing Progressive and Interactive Analytics Processes for High-Dimensional Data Analysis

In interactive data analysis processes, the dialogue between the human and the computer is the enabling mechanism that can lead to actionable observations about the phenomena being investigated. It is of paramount importance that this dialogue is not interrupted by slow computational mechanisms that do not consider any known temporal human-computer interaction characteristics that prioritize the perceptual and cognitive capabilities of the users. In cases where the analysis involves an integrated computational method, for instance to reduce the dimensionality of the data or to perform clustering, such non-optimal processes are often likely. To remedy this, progressive computations, where results are iteratively improved, are getting increasing interest in visual analytics. In this paper, we present techniques and design considerations to incorporate progressive methods within interactive analysis processes that involve high-dimensional data. We define methodologies to facilitate processes that adhere to the perceptual characteristics of users and describe how online algorithms can be incorporated within these. A set of design recommendations and according methods to support analysts in accomplishing high-dimensional data analysis tasks are then presented. Our arguments and decisions here are informed by observations gathered over a series of analysis sessions with analysts from finance. We document observations and recommendations from this study and present evidence on how our approach contribute to the efficiency and productivity of interactive visual analysis sessions involving high-dimensional data

An information theoretic approach to camera control for crowded scenes

Navigation and monitoring of large and crowded virtual environments is a challenging task and requires intuitive camera control techniques to assist users. In this paper, we present a novel automatic camera control technique providing a scene analysis framework based on information theory. The developed framework contains a probabilistic model of the scene to build entropy and expectancy maps. These maps are utilized to find interest points which represent either characteristic behaviors of the crowd or novel events occurring in the scene. After an interest point is chosen, the camera is updated accordingly to display this point. We tested our model in a crowd simulation environment and it performed successfully. Our method can be integrated into existent camera control modules in computer games, crowd simulations and movie pre-visualization applications

An information theoretic approach to camera control for crowded scenes

Navigation and monitoring of large and crowded virtual environments is a challenging task and requires intuitive camera control techniques to assist users. In this paper, we present a novel automatic camera control technique providing a scene analysis framework based on information theory. The developed framework contains a probabilistic model of the scene to build entropy and expectancy maps. These maps are utilized to find interest points which represent either characteristic behaviors of the crowd or novel events occurring in the scene. After an interest point is chosen, the camera is updated accordingly to display this point. We tested our model in a crowd simulation environment and it performed successfully. Our method can be integrated into existent camera control modules in computer games, crowd simulations and movie pre-visualization applications

An information theoretical approach to crowd simulation

In this study, an information theory based framework to automatically construct analytical maps of crowd’s locomotion, called behavior maps, is presented. For these behavior maps, two distinct use cases in crowd simulation domain are introduced; i) automatic camera control ii) behavioral modeling. The first use case for behavior maps is an automatic camera control technique to display interest points which represent either characteristic behavior of the crowd or novel events occurring in the scene. As the second use case, a behavioral model to control agents’ behavior with agent-crowd interaction formulations is introduced. This model can be integrated into a crowd simulator to enhance its behavioral complexity and realism

Temporal dynamics of user interests in web search queries

Web search query logs contain valuable information which can be utilized for personalization and improvement of search engine performance. The aim in this paper(1) is to cluster users based on their interests, and analyze the temporal dynamics of these clusters. In the proposed approach, we first apply clustering techniques to group similar users with respect to their web searches. Anticipating that the small number of query terms used in search queries would not be sufficient to obtain a proper clustering scheme, we extracted the summary content of the clicked web page from the query log. In this way, we enriched the feature set more efficiently than the content crawling. We also provide preliminary survey results to evaluate clusters. Clusters may change with the user flow from one cluster to the other as time passes. This is due to the fact that users' interests may shift over time. We used statistical methods for the analysis of temporal changes in users' interests. As a case study, we experimented on the query logs of a search engine

Integrating information theory in agent-based crowd simulation behavior models

Crowds must be simulated believable in terms of their appearance and behavior to improve a virtual environment's realism. Due to the complex nature of human behavior, realistic behavior of agents in crowd simulations is still a challenging problem. In this paper, we propose a novel behavioral model which builds analytical maps to control agents' behavior adaptively with agent-crowd interaction formulations. We introduce information theoretical concepts to construct analytical maps automatically. Our model can be integrated into crowd simulators and enhance their behavioral complexity. We made comparative analyses of the presented behavior model with measured crowd data and two agent-based crowd simulators