5天掌握大语言 Agent：Day 2 基于大语言模型的智能体结构

Day 2: The Structure of LLM-based Agents

Goal: Understand the internal structure of AI agents, focusing on how LLMs serve as the “brain,” and how agents perceive and act in their environments.

Topics:

The “Brain” of AI Agents: LLMs as the Core

• Description: The large language model (LLM) acts as the “brain” of AI agents. It processes input, generates responses, and makes decisions based on data. GPT-4 and similar models use deep neural networks to simulate human-like reasoning and communication.
• Core Function: LLMs use their pre-trained knowledge to process language, understand context, and provide intelligent responses to queries or tasks. These models are particularly good at handling complex multi-turn dialogues and solving tasks based on natural language instructions.

Perception: How AI Agents “Sense” Their Environment

• Description: Perception is the way AI agents gather information from their environment. This could include textual data (like user inputs), visual data (images or videos), or even sensor data (in physical robots).
• Multi-Modal Perception: Some agents combine different types of perception (text, visual, and sensory data). For example, an AI agent could interpret images alongside textual commands to make more informed decisions.
• Key Role: LLM-based agents primarily use Natural Language Processing (NLP) for textual perception, but with advancements in multi-modal models, they can now also interpret images, sounds, or other inputs.

Action: How AI Agents Interact with Their Environment or Humans

• Description: Action refers to the responses or tasks AI agents perform after processing input. Actions can range from providing a textual response (in a chatbot) to controlling physical devices (in robotics).
• Feedback Loops: In many systems, agents adjust their actions based on feedback from their environment or users. This loop of perception, action, and feedback helps agents learn and adapt over time.

第二天：基于大语言模型的智能体结构

目标：理解智能体的内部结构，重点探讨大语言模型如何作为“核心大脑”，以及智能体如何感知环境并采取行动。

主题：智能体的“大脑”：大语言模型充当核心

• 描述：大语言模型充当智能体的“大脑”，负责处理输入、生成响应，并基于数据做出决策。GPT-4及类似模型利用深度神经网络来模拟类人的推理和交流能力。

核心功能：

• 大语言模型通过其预训练知识处理自然语言，理解上下文，并对查询或任务提供智能响应。
• 这些模型尤其擅长处理复杂的多轮对话，并能依据自然语言指令解决任务。

感知：智能体如何“感知”其环境

描述：感知是智能体从环境中收集信息的方式。感知信息可以包括：

• 文本数据（如用户输入的文本指令）
• 视觉数据（如图像或视频）
• 传感器数据（如物理机器人收集的环境数据）

多模态感知（Multi-Modal Perception）：

• 一些智能体能够结合多种感知方式（文本、视觉和传感器数据）。例如，一个 智能体可以在接收文本指令的同时解析图像，以便做出更准确的决策。

关键作用：

• 基于LLM的代理主要依靠自然语言处理（NLP）进行文本感知。
• 随着多模态模型的发展，LLMs 现在也能够解析图像、声音等其他输入形式，使代理的感知能力更加全面。

行动：智能体如何与环境或人类交互

描述：行动指智能体在处理输入后执行的响应或任务。具体行动可以包括：

• 提供文本回应（如聊天机器人回复用户问题）
• 控制物理设备（如机器人执行操作）

反馈循环（Feedback Loops）：

• 在许多系统中，智能体会根据环境或用户的反馈调整其行动。
• 这种感知—行动—反馈的循环机制，使 智能体能够不断学习和适应，从而优化其决策和任务执行能力。