18.2 参考文献

核心学术文献

  1. Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
    • 经典教材，涵盖智能体架构与基础理论
  2. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
    • 强化学习在智能体决策中的权威指南
  3. Goodfellow, I., et al. (2016). Deep Learning. MIT Press.
    • 深度学习技术及其在智能体感知模块的应用

技术白皮书与行业报告

  1. Google Research (2023). "Pathways Language Model (PaLM): Scaling to 540B Parameters".
    • 大语言模型在对话型智能体中的前沿进展
  2. McKinsey & Company (2022). The State of AI in 2022: Generative AI Breaks the Mold.
    • 行业应用趋势与商业案例分析

伦理与政策研究

  1. Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
    • 探讨自主智能体的长期风险
  2. EU AI Act (2021). Proposal for a Regulation on Artificial Intelligence. European Commission.
    • 现行AI治理框架的法规文本

开源项目文档

  1. TensorFlow Team (2023). Agents: A Library for Reinforcement Learning in TensorFlow. [GitHub]
    • 智能体开发框架的官方实现指南
  2. OpenAI (2023). GPT-4 Technical Report.
    • 生成式智能体的架构细节

补充阅读

  1. 期刊特辑: Nature Machine Intelligence (2023). "Multi-Agent Systems in Healthcare".
  2. 会议论文: NeurIPS 2022 Proceedings. "Emergent Cooperation in AI Agent Swarms".

提示：建议读者通过DOI或arXiv编号检索最新版本，部分资源可通过作者官网或学术平台（如Google Scholar）获取。

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