Journal
ENVIRONMENTAL MODELLING & SOFTWARE
Volume 166, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.envsoft.2023.105713
Keywords
Agent-based modeling; Modeling agent decisions and actions; Artificial intelligence; Machine learning; Data science
Ask authors/readers for more resources
This article discusses the wide use of Agent-based modeling (ABM) in various disciplines and practice domains, as well as the advances and challenges in understanding agent behaviors. The authors emphasize the potential revolutionary impacts of artificial intelligence and data science in understanding agent decisions and behaviors in complex systems. They propose an innovative approach using reinforcement learning and convolutional neural networks to equip agents with self-learning intelligence. The article calls for further developments of ABM, particularly in modeling agent behaviors, with the integration of data science and artificial intelligence.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available