Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Interest management middleware for networked games
Proceedings of the 2005 symposium on Interactive 3D graphics and games
An Adaptive Interest Management Scheme for Distributed Virtual Environments
Proceedings of the 19th Workshop on Principles of Advanced and Distributed Simulation
Journal of Computational Physics
Distributed simulation of agent-based systems with HLA
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A3: A Novel Interest Management Algorithm for Distributed Simulations of MMOGs
DS-RT '08 Proceedings of the 2008 12th IEEE/ACM International Symposium on Distributed Simulation and Real-Time Applications
Energy Consumption of Mobile YouTube: Quantitative Measurement and Analysis
NGMAST '08 Proceedings of the 2008 The Second International Conference on Next Generation Mobile Applications, Services, and Technologies
A framework of evaluating partitioning mechanisms for agent-based simulation systems
SpringSim '09 Proceedings of the 2009 Spring Simulation Multiconference
A comparative study of partitioning methods for crowd simulations
Applied Soft Computing
Cluster based partitioning for agent-based crowd simulations
Winter Simulation Conference
Transactions on computational science XII
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Agent-based crowd simulation, which aims to simulate large crowds of autonomous agents with realistic behavior, is a challenging but important problem. One key issue is scalability. Parallelism and distribution is an obvious approach to achieve scalability for agent-based crowd simulation. Parallel and distributed agent-based crowd simulation, however, introduces its own challenges, in particular, effectively distributing workload amongst multiple nodes with minimal overhead. In order to ensure effective distribution with low overhead, a proper partitioning mechanism is required. Generally, human crowds consist of groups or exhibit particular patterns of flow, which are then reflected in simulations. Exploiting this grouping with an appropriate partitioning mechanism should enable efficient distribution of crowd simulation. In this paper we introduce a grid-based clustering algorithm which we compare to previous clustering approaches that used the K-means algorithm.