A Review of Reasoning in Artificial Agents Using Large Language Models
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Date
2025-01-07
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1386
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Abstract
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.
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Explainable Artificial Intelligence (XAI), agent, llm, reasoning, symbolic, testing
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10
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Proceedings of the 58th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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