A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Neural network learning and expert systems
Neural network learning and expert systems
HCSM: a framework for behavior and scenario control in virtual environments
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on graphics, animation, and visualization for simulation environments
Enhanced presence in driving simulators using autonomous traffic with virtual personalities
Presence: Teleoperators and Virtual Environments
Crowdbrush: interactive authoring of real-time crowd scenes
SCA '04 Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation
A Distributed Approach for Coordination of Traffic Signal Agents
Autonomous Agents and Multi-Agent Systems
Adaptive game AI with dynamic scripting
Machine Learning
A behavioral multi-agent model for road traffic simulation
Engineering Applications of Artificial Intelligence
A Normative Model for Behavioral Differentiation
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
A multiagent approach to autonomous intersection management
Journal of Artificial Intelligence Research
Unique Character Instances for Crowds
IEEE Computer Graphics and Applications
A development environment using behavior patterns to facilitate building 3D/VR applications
Proceedings of the Sixth Australasian Conference on Interactive Entertainment
Integration of Driving and Traffic Simulation: Issues and First Solutions
IEEE Transactions on Intelligent Transportation Systems
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In individual-centered simulations, the variety and consistency of agents' behaviors reinforce the realism and validity of the simulation. Variety increases the diversity of behaviors that users meet during the simulation. Consistency ensures that these behaviors improve the users' feeling of immersion. In this work, we address the issue of the simultaneous influence of these two elements. We propose a formalization of the construction of populations for agent-based simulations, which provides the basis for a generic and non-intrusive tool allowing an out-of-the-agent design. First, the model uses behavioral patterns to describe standards of behaviors for the agents. They provide a behavioral archetype during agents' creation, and are also a compliance reference, that allows to detect deviant behaviors and address them. Then, a specific process instantiates the agents by using the specification provided by the patterns. Finally, inference enables to automate behavioral patterns configuration from real or simulated data. This formalization allows for the easy introduction of variety in agents' behaviors, while controlling the conformity to specifications. We applied the model to traffic simulation, in order to introduce driving styles specified using behavioral patterns (e.g. cautious or aggressive drivers). The behavioral realism of the traffic was therefore improved, and the experimentations we conducted show how the model contributes to increase the variety and the representativeness of the behaviors.