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

Real-Time Evacuation Simulation in Mine Interior Model of Smoke and Action

Virtual human crowd models have been used in the simulation of building and urban evacuation, but have not yet applied to underground coal mine operations and escape situations with emphasis on smoke, fires and physiological behaviors. We explore this through a real-time simulation model, MIMOSA (Mine Interior Model Of Smoke and Action), which integrates an underground coal mine virtual environment, a fire and smoke propagation model, and a human physiology and behavior model. Each individual agent has a set of physiological parameters as variables of time and environment, simulating a miner’s physiological condition during normal operations as well as during emergencies due to fire and smoke. To obtain appropriate agent navigation in the mine environment, we have extended the HiDAC framework (High- Density Autonomous Crowds) navigation from a grid-based cell-portal graph to a geometrybased portal path and integrated a novel cellportal and shortest path visibility algorithm

Modeling Family Behaviors in Crowd Simulation

Modeling human behavior for a general situation is difficult, if not impossible. Crowd simulation represents one of the approaches most commonly used to model such behavior. It is mainly concerned with modeling the different human structures incorporated in a crowd. These structures could comprise individuals, groups, friends, and families. Various instances of these structures and their corresponding behaviors are modeled to predict crowd responses under certain circumstances and to subsequently improve event management, facility and emergency planning. Most currently existing modeled behaviors are concerned with depicting individuals as autonomous agents or groups of agents in certain environments. This research focuses on providing structural and state-based behavioral models for the concept of a family incorporated in the crowd. The structural model defines parents, teenagers, children, and elderly as members of the family. It also draws on the associated interrelationships and the rules that govern them. The behavioral model of the family encompasses a number of behavioral models associated with the triggering of certain well-known activities that correspond to the family’s situation. For instance, in normal cases, a family member(s) may be hungry, bored, or tired, may need a restroom, etc. In an emergency case, a family may experience the loss of a family member(s), the need to assist in safe evacuation, etc. Activities that such cases trigger include splitting, joining, carrying children, looking for family member(s), or waiting for them. The proposed family model is implemented on top of the RVO2 library that is using agent-based approach in crowd simulation. Simulation case studies are developed to answer research questions related to various family evacuation approaches in emergency situations

Agent-based Crowd Simulation Modelling for a Gaming Environment

Crowd simulation study has become a favorite subject in the computer graphics community in the past three decades. It usually is a sub-function within many applications such as video games, films, and public security. This thesis proposes an independent crowd simulation model that is capable of running an Agent-based method through a gaming environment. It can simulate realistic human crowds with user-controllable features to provide a gaming-like experience. Our approach features an enhanced rendering system based on Distinguishable Agents Generating Method (DAGM). This method can generate distinguishable and scalable 3D human models in real-time. We also introduce our Multi-layer Collision System (MCS), which features a collision-message collection system and an evaluation processing system. We also introduce Building & City-planning Generating System (BCGS) for the purpose of setting up obstacles for the crowd during an evacuation simulation. Moreover, in this thesis, we also extend the study to other aspects such as crisis training and human animations to provide a complete agent-based crowd simulation model

Crowd Simulation Incorporating Agent Psychological Models, Roles and Communication

We describe a new architecture to integrate a psychological model into a crowd simulation system in order to obtain believable emergent behaviors. Our existing crowd simulation system (MACES) performs high level wayfinding to explore unknown environments and obtain a cognitive map for navigation purposes, in addition to dealing with low level motion within each room based on social forces. Communication and roles are added to achieve individualistic behaviors and a realistic way to spread information about the environment. To expand the range of realistic human behaviors, we use a system (PMFserv) that implements human behavior models from a range of ability, stress, emotion, decision theoretic and motivation sources. An architecture is proposed that combines and integrates MACES and PMFserv to add validated agent behaviors to crowd simulations

Modeling Crowd and Trained Leader Behavior during Building Evacuation

This article considers animating evacuation in complex buildings by crowds who might not know the structure\u27s connectivity, or who find routes accidently blocked. It takes into account simulated crowd behavior under two conditions: where agents communicate building route knowledge, and where agents take different roles such as trained personnel, leaders, and followers