Hauff M, Lurz A (2022)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2022
Event location: Taipei-Sydney
URI: https://aisel.aisnet.org/pacis2022/106
Simulations of behavior, in particular agent-based models (ABM), enhance informed decision-making. At present, Covid-19's autonomous dispersion is a notable use case, but frequent applications of the method include analyses of energy networks, traffic, and pedestrian movement. To enable an even more advanced architecture, artificial intelligence (AI) is effectively superimposed on top of the ABMs. This paper focuses on examining the state of the art of ABM enhanced by AI and its application areas. AI is applied in various ways at the different stages of the ABM development process. In current research, the main attempt is to implement AI into the agent. For this purpose, the agent's behavioral rules are enhanced or even replaced by AI structures. These mechanisms can range from simple pre-defined decisions to well-designed reward functions through which the agent learns to optimize its decisions and achieve the best possible performance.
APA:
Hauff, M., & Lurz, A. (2022). Agent-Based Models Using Artificial Intelligence: A Literature Review. In Proceedings of the Pacific Asia Conference on Information Systems (PACIS) 2022. Taipei-Sydney.
MLA:
Hauff, Marco, and Annika Lurz. "Agent-Based Models Using Artificial Intelligence: A Literature Review." Proceedings of the Pacific Asia Conference on Information Systems (PACIS) 2022, Taipei-Sydney 2022.
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