Architecture and performance evaluation of a massive multi-agent system
Proceedings of the third annual conference on Autonomous Agents
Communications of the ACM - How the virtual inspires the real
Cormas: Common-Pool Resources and Multi-agent Systems
IEA/AIE '98 Proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial In telligence and Expert Systems: Tasks and Methods in Applied Artificial Intelligence
Scenario description for multi-agent simulation
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Transcendent communication: location-based guidance for large-scale public spaces
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
FreeWalk/Q: social interaction platform in virtual space
Proceedings of the ACM symposium on Virtual reality software and technology
Modeling agents and interactions in agricultural economics
AAMAS '06 Proceedings of the fifth 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
Multi-agent based simulation: where are the agents?
MABS'02 Proceedings of the 3rd international conference on Multi-agent-based simulation II
Modeling agents and interactions in agricultural economics
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Multi-agent simulations in decision support: specifics of the biological incident management
MCBANTA'11 Proceedings of the 12th WSEAS international conference on Mathematics and computers in biology, business and acoustics
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To realize large scale socially embedded systems, this paper proposes a multiagent-based participatory design that consists of steps called 1) participatory simulation, where scenario-guided agents and human-controlled avatars coexist in virtual space and jointly perform simulations, and 2) augmented experiment, where an experiment is performed in real space by human subjects and scenario-guided extras. In this methodology, we use production rules to describe agent models for approximating users, and multiagent scenarios to describe interaction models among services and their users. To learn agent and interaction models incrementally from simulations and experiments, we establish the participatory design loop with deductive machine learning technologies.