Abstract
Simulation models are widely used to study complex systems. Current simulation models are generally handcrafted using expert knowledge (knowledge-driven); however, this process is slow and introduces modeler bias. This article presents an approach towards data-driven simulation modeling by developing a framework that discovers simulation models in an automated way for mobile agent-based applications. The framework is comprised of three components: (1) a model space specification, (2) a search method (genetic algorithm), and (3) framework measurement metrics. The model space specification provides a formal specification for the general model structure from which various models can be generated. The search method is used to efficiently search the model space for candidate models that exhibit desired behavior patterns. The five framework measurement metrics: flexibility, comprehensibility, controllability, composability, and robustness, are developed to evaluate the overall framework. The results demonstrate that it is possible to discover a variety of interesting models using the framework.
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