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1、AgentVerse:语模型智能体合作框架杨成北京邮电学 计算机学院2023.11.26ContentsBackgroundWarmup:SmallvilleSimulationTask-SolvingContentsBackgroundWarmup:SmallvilleSimulationTask-Solvingn What is Agent?n Why LLM is suitable for agents?n The Ability of Single AgentContentsBackgroundBackgroundAn Al model that can take concrete a

2、ction interacting with the outside world.-WikipediaAn agent is a computer system that is situated in some environment,and that is capable of autonomous action inthis environment in order to meet its design objectives.-Wooldridge&JenningsAn agent is anything that can be viewed as perceiving its envir

3、onment through sensors and acting upon that environment through actuators.-Russell and NorvigBackgroundBackgroundWhat is agent?BackgroundBackgroundThe ability of single-agentNatural language interactionuHigh-quality natural language generation:exceptional natural language generation capabilitiesuMul

4、ti-turn interactive conversation:The foundation of effective and consistent communicationuIntention and implication understanding:incapable of emulating human dialogues or fully leveraging the information Xi,Zhiheng et al.“The Rise and Potential of Large Language Model Based Agents:A Survey.”arXiv,h

5、ttps:/arxiv.org/abs/2309.07864.Bang,Y.,S.Cahyawijaya,N.Lee,et al.A multitask,multilingual,multimodal evaluation of chatgpt on reasoning,hallucination,and interactivity.CoRR,abs/2302.04023,2023Wang,Z.,G.Zhang,K.Yang,et al.Interactive natural language processing.CoRR,abs/2305.13246,2023BackgroundBackg

6、roundThe ability of single-agentReasoninguChain of Thought:Lets think step by step.uLeast to Most:break down a complex problem into a series of simpler subproblems and then solve them in sequence.uSelf-Refine:improving initial outputs from LLMs through iterative feedback and refinement was proposed.

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本文主要探讨了大型语言模型智能体合作框架AgentVerse的研究与应用。AgentVerse是一个多智能体协作框架,包括四个阶段:智能体招募、协作决策制定、行动执行和评估与评估。通过模拟约翰·林的一天,展示了智能体如何感知环境、进行自然语言交互、推理、规划和工具使用。在协作决策制定和行动执行阶段,智能体通过对话和自我反思来优化任务分配和提高任务完成效率。在评估与评估阶段,智能体对解决方案进行最终评估,并提供反馈。此外,文章还讨论了竞争性和非竞争性模拟实验的结果,以及多智能体团队与单一智能体在完成任务方面的比较。多智能体团队在对话能力、数学计算、逻辑推理和代码生成方面表现出更高的能力。最后,文章提出了一个具体领域的智能体架构ChatDev,用于软件开发,并通过案例展示了其应用。
如何实现多智能体协作?" 智能体如何应对复杂环境?" 如何通过聊天机器人进行软件开发?"
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