Parameter estimation is a common challenge that arises in the domain of computational scientific modeling. Agent-based models offer particular challenges in this regard, and many solutions are too computationally intense and scale with the number of parameters. In this paper, we propose knowledge-based function approximation methods to deal with this problem in agent-based modeling. Our method is implemented within the VERA modeling system, and we show the validity of our methods using an internal model as well as an external model.
Citation
Broniec, W., An, S., Rugarber, S., Goel, A. (2021) Guiding Parameter Estimation of Agent-Based Modeling Through Knowledge-based Function Approximation. Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021) - Stanford University, Palo Alto, California, USA, March 22-24, 2021.
