Intelligent Management of Data Driven Simulations to Support Model Building in the Social Sciences

Catriona Kennedy1 and Georgios Theodoropoulos1

1 School of Computer Science, University of Birmingham, UK
cmk@cs.bham.ac.uk
gkt@cs.bham.ac.uk

Abstract. 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 con sider 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 dat a 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.

Key words: agent, cognition, decision support, fault-tolerance, simulation, social sciences.

LNCS 3993, pp. 562-569.

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