Crowd Simulation in 3D Virtual Environments Somnuk Phon-Amnuaisuk 1,3 , Ahmad Rafi 2 , Thien-Wan Au 3 , Saiful Omar 3 and Nyuk-Hiong Voon 3 1 Media Informatics Special Interest Group, 1 Centre for Innovative Engineering, Universiti Teknologi Brunei, 2 Faculty of Creative Multimedia, Multimedia University, Malaysia, 3 School of Computing & Informatics, Universiti Teknologi Brunei. somnuk.phonamnuaisuk@utb.edu.bn, rafi@mmu.edu.my, {twan.au,saiful.omar,jennifer.voon}@utb.edu.bn Abstract. Realistic animation of agents’ activities in a 3D virtual envi- ronment has many useful applications, for examples, creative industries, urban planning, military simulation and disaster management. It is te- dious to manually pre-program each agent’s actions, its interactions with other agents and with the environment. Simulation is a good approach in this kind of domain since complex global behaviors emerge from the local interactions. We simulate a crowd movement using a multi-agent approach where each agent is situated in the virtual environment. An agent can perceive and interact with other agents and with the envi- ronment. Complex behaviors emerging from these interactions are from local rules and without any central control. These behaviors reveal the complexity of the domain without explicitly programming the system. In this work, we investigate (i) the navigation of the agents and (ii) the corresponding animations of each agent’s behaviors. Simulation results under different parameters are presented and discussed. Keywords Crowd simulation, Boids framework, Behavior-based animation, 3D virtual environments 1 Introduction Given a complex system, where there are interactions among components in the system, it is a great challenge to define the underlining functions governing the complex interactions among them. Contemporary works in this area favor the explanation of complex interactions based on the self-organising paradigm. Complex behaviors observed in a self-organising model emerges from simple interactions among the components in the system without any central control. For example, fish schooling and bird flocking can be successfully modelled based on simple local rules such as the one proposed in the Boid framework [1]. Simulation is an effective approach to model a complex system since emerg- ing complex behaviors in the system are not required to be pre-programmed; in- stead, they emerge from the simulation process. Computational approaches such