Performance evaluation of an agent server capable of hosting large numbers of agents
Proceedings of the fifth international conference on Autonomous agents
Meta-level Architecture for Executing Multi-agent Scenarios
Proceedings of the 5th Pacific Rim International Workshop on Multi Agents: Intelligent Agents and Multi-Agent Systems
Towards Truly Agent-Based Traffic and Mobility Simulations
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Using emergence in participatory simulations to design multi-agent systems
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Modeling human behavior for virtual training systems
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Proceedings of the First international conference on Massively Multi-Agent Systems
MMAS'04 Proceedings of the First international conference on Massively Multi-Agent Systems
Participatory technologies for designing ambient intelligence systems
Journal of Ambient Intelligence and Smart Environments
Participatory technologies for designing ambient intelligence systems
Journal of Ambient Intelligence and Smart Environments
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Various situations in a massively multi-agent simulation will emerge in a simulation or the period of the simulation will become too long. These situations cause problems for system operators in that each action scenario becomes too complex to maintain and a simulation costs very long time. Therefore, flexible control of the simulation, such as changing simulation speed and switching agents' action scenarios, is required. We propose a meta-scenario description language and a meta-level control architecture. The meta-scenario description language describes how to control simulations and agents based on an extended finite state machine. Meta-level control architecture achieves control on the basis of meta-scenarios provided by a meta-scenario interpreter, which controls interpreters of agents' action scenarios and the simulation environment. In addition, our proposed architecture does not lose scalability of massively multi-agent systems for some applications.