Artificial Intelligence


Deep Caching for Agent-Based Analysis and Optimization

Authors: Rune Vestvik

Agent-based modeling and simulation is a "bottomup" approach for system research, optimization and decision support. The method consist of mapping real world processes intoadigitaltwinmodel[1]andperformingrisk-freeexperiments on the digital twin before applying changes back into real world scenarios. The need for computation resources increases as models grow more detailed. This fact, combined with high stochasticity and the use of Monte Carlo simulations, slows down the processes of performing sensitivity analysis and model optimization. This paper introduces a method for caching agent-based simulations for increased performance and shorting of the feedback loop.

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[v1] 2019-08-15 05:17:31

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