Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
The increasing sophistication and the use of large language models (LLMs) in artificial agents highlights the need to investigate their reasoning capabilities and limitations. Understanding these aspects is crucial, given the integral role of reasoning in decision-making processes, which are central to a software or embodied agent. This research paper presents a systematic review of the topic. We review the literature by selecting and analyzing highly cited papers using both PRISMA and snowballing. The gathered literature is categorized using a detailed framework of facets and categories. In the results section, we elaborate on our findings and illustrate the mapping through bubble chart visualizations. The paper concludes by highlighting research gaps and suggesting directions for future studies.
Recommended Citation
Naidu, Nagraj and El-Gayar, Omar, "A Review of Reasoning in Artificial Agents Using Large Language Models" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 2.
https://aisel.aisnet.org/hicss-58/da/xai/2
A Review of Reasoning in Artificial Agents Using Large Language Models
Hilton Waikoloa Village, Hawaii
The increasing sophistication and the use of large language models (LLMs) in artificial agents highlights the need to investigate their reasoning capabilities and limitations. Understanding these aspects is crucial, given the integral role of reasoning in decision-making processes, which are central to a software or embodied agent. This research paper presents a systematic review of the topic. We review the literature by selecting and analyzing highly cited papers using both PRISMA and snowballing. The gathered literature is categorized using a detailed framework of facets and categories. In the results section, we elaborate on our findings and illustrate the mapping through bubble chart visualizations. The paper concludes by highlighting research gaps and suggesting directions for future studies.
https://aisel.aisnet.org/hicss-58/da/xai/2