Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.1016/j.envsoft.2023.105713
Titel (primär) Modeling agent decision and behavior in the light of data science and artificial intelligence
Autor An, L.; Grimm, V.; Bai, Y.; Sullivan, A.; Turner II, B.L.; Malleson, N.; Heppenstall, A.; Vincenot, C.; Robinson, D.; Ye, X.; Liu, J.; Lindkvist, E.; Tang, W.
Quelle Environmental Modelling & Software
Erscheinungsjahr 2023
Department OESA
Band/Volume 166
Seite von art. 105713
Sprache englisch
Topic T5 Future Landscapes
Abstract Agent-based modeling (ABM) has been widely used in numerous disciplines and practice domains, subject to many eulogies and criticisms. This article presents key advances and challenges in agent-based modeling over the last two decades and shows that understanding agents’ behaviors is a major priority for various research fields. We demonstrate that artificial intelligence and data science will likely generate revolutionary impacts for science and technology towards understanding agent decisions and behaviors in complex systems. We propose an innovative approach that leverages reinforcement learning and convolutional neural networks to equip agents with the intelligence of self-learning their behavior rules directly from data. We call for further developments of ABM, especially modeling agent behaviors, in the light of data science and artificial intelligence.
dauerhafte UFZ-Verlinkung https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=27040
An, L., Grimm, V., Bai, Y., Sullivan, A., Turner II, B.L., Malleson, N., Heppenstall, A., Vincenot, C., Robinson, D., Ye, X., Liu, J., Lindkvist, E., Tang, W. (2023):
Modeling agent decision and behavior in the light of data science and artificial intelligence
Environ. Modell. Softw. 166 , art. 105713 10.1016/j.envsoft.2023.105713