将竞争转化为协作：多智能体系统与语言模型在现代组织中的革命性作用
Transforming Competition into Collaboration: The Revolutionary Role of Multi-Agent Systems and Language Models in Modern Organizations
AI总结：
本文提出基于多智能体系统与大型语言模型的智能体，通过角色扮演和引导对话将竞争转为协作，以支持组织战略决策，并展示其适应不同应用与复杂性的潜力。
摘要：
本文探讨了基于多智能体系统理论（SMA）与大型语言模型（LLM）相结合的计算实体的动态影响，这些实体以其模拟复杂人类互动的能力为特征，作为一种可能性，通过使用专门的人工智能体来彻底改变人类用户的交互，从支持运营层面的组织流程到基于应用知识和人类编排的战略决策。先前的研究揭示了局限性，特别是在人工智能体的自主方法中，尤其是在处理新挑战和务实任务（如诱导逻辑推理和问题解决）时。还考虑到传统技术，如思维链的刺激，需要明确的人类指导。在我们的方法中，我们采用了基于大型语言模型（LLM）开发的智能体，每个智能体具有考虑行为元素的不同原型设计，由基于场景（角色扮演）业务中提出的用例刺激知识生成的策略驱动，采用智能体之间的讨论方法（引导对话）。我们展示了基于多智能体系统理论（SMA）和基于大型语言模型（LLM based）的创新用途开发对组织战略有用的智能体的潜力，提供了一种差异化和适应性强的实验，适用于不同的应用、复杂性、领域和LLM能力。
英文摘要：
This article explores the dynamic influence of computational entities based on multi-agent systems theory (SMA) combined with large language models (LLM), which are characterized by their ability to simulate complex human interactions, as a possibility to revolutionize human user interaction from the use of specialized artificial agents to support everything from operational organizational processes to strategic decision making based on applied knowledge and human orchestration. Previous investigations reveal that there are limitations, particularly in the autonomous approach of artificial agents, especially when dealing with new challenges and pragmatic tasks such as inducing logical reasoning and problem solving. It is also considered that traditional techniques, such as the stimulation of chains of thoughts, require explicit human guidance. In our approach we employ agents developed from large language models (LLM), each with distinct prototyping that considers behavioral elements, driven by strategies that stimulate the generation of knowledge based on the use case proposed in the scenario (role-play) business, using a discussion approach between agents (guided conversation). We demonstrate the potential of developing agents useful for organizational strategies, based on multi-agent system theories (SMA) and innovative uses based on large language models (LLM based), offering a differentiated and adaptable experiment to different applications, complexities, domains, and capabilities from LLM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
