Families of models that cross levels of resolution: issues for design, calibration and management
WSC '93 Proceedings of the 25th conference on Winter simulation
Consistency maintenance in multiresolution simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Ruminations on the Implications of Multi-Resolution Modeling on DIS/HLA
DIS-RT '99 Proceedings of the 3rd International Workshop on Distributed Interactive Simulation and Real-Time Applications
Multi-Resolution Network Simulations Using Dynamic Component Substitution
MASCOTS '01 Proceedings of the Ninth International Symposium in Modeling, Analysis and Simulation of Computer and Telecommunication Systems
Scalable pedestrian simulation for virtual cities
Proceedings of the ACM symposium on Virtual reality software and technology
Simulation Level of Detail for Virtual Humans
IVA '07 Proceedings of the 7th international conference on Intelligent Virtual Agents
Extending the Soar Cognitive Architecture
Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
A unified cognitive architecture for physical agents
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Dynamic level of detail for large scale agent-based urban simulations
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
An interaction-oriented model for multi-scale simulation
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
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Large-scale simulations often use multiple agent representations to permit the study of specific multi-agent phenomena, and to find a balance between run-time performance and level of detail of the simulation. Although these approaches are effective, they do not always offer the desired level of analysis, especially when this level is between the resolutions of the models available. In this paper, we aim at offering a finer resolution in exploring this trade-off by introducing an intermediate level between two given resolutions, which can apply to all agent models and allows a more progressive transition to offer the desired level of analysis. We introduce a framework for such a methodology and evaluate it through the extension of an existing approach, along two criteria: its impact on computational resources, and an estimate of the dissimilarity between a simulation using our methodology and one without. Initial experiments show that consistency is almost maintained while CPU gain varies from low to significant depending on the context.