CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Artificial Intelligence
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Grounded semantic composition for visual scenes
Journal of Artificial Intelligence Research
From KISS to KIDS: an 'anti-simplistic' modelling approach
MABS'04 Proceedings of the 2004 international conference on Multi-Agent and Multi-Agent-Based Simulation
Towards dynamically adaptive weather analysis and forecasting in LEAD
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
Integrating fire, structure and agent models
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
An Agent-Based Environment for Simulation Model Composition
Proceedings of the 22nd Workshop on Principles of Advanced and Distributed Simulation
AIMSS: An Architecture for Data Driven Simulations in the Social Sciences
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Symbiotic Simulation Control in Semiconductor Manufacturing
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part III
An online transportation system simulation testbed
Winter Simulation Conference
Research issues in symbiotic simulation
Winter Simulation Conference
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Artificial intelligence (AI) can contribute to the management of a data driven simulation system, in particular with regard to adaptive selection of data and refinement of the model on which the simulation is based. We consider two different classes of intelligent agent that can control a data driven simulation: (a) an autonomous agent using internal simulation to test and refine a model of its environment and (b) an assistant agent managing a data-driven simulation to help humans understand a complex system (assisted model-building). In the first case the agent is situated in its environment and can use its own sensors to explore the data sources. In the second case, the agent has much less independent access to data and may have limited capability to refine the model on which the simulation is based. This is particularly true if the data contains subjective statements about the human view of the world, such as in the social sciences. For complex systems involving human actors, we propose an architecture in which assistant agents cooperate with autonomous agents to build a more complete and reliable picture of the observed system.