将机器学习融入理性智能体，提升决策能力
Integrating Machine Learning into Belief-Desire-Intention Agents: Current Advances and Open Challenges
- 以信念-欲望-意图架构为框架，系统整合机器学习
- 揭示现有方法在表达能力和一致性上的不足
- 适合研究智能体架构与人机交互的学者参考
得益于机器学习模型在感知和认知任务中表现出的类人能力，将机器学习嵌入理性智能体架构的框架日益受到关注。然而，当前研究仍呈现碎片化状态，常仅将机器学习简单嵌入通用智能体结构，忽视了理性架构（如信念-欲望-意图，BDI）的表达优势。本文以BDI范式为参照，对现有方法进行细粒度系统梳理，揭示了理性智能体融合机器学习的快速演进趋势，并识别出设计高效理性机器学习智能体的关键研究机遇与开放挑战。
原文摘要 · Abstract (English)
Thanks to the remarkable human-like capabilities of machine learning (ML) models in perceptual and cognitive tasks, frameworks integrating ML within rational agent architectures are gaining traction. Yet, the landscape remains fragmented and incoherent, often focusing on embedding ML into generic agent containers while overlooking the expressive power of rational architectures--such as Belief-Desire-Intention (BDI) agents. This paper presents a fine-grained systematisation of existing approaches, using the BDI paradigm as a reference. Our analysis illustrates the fast-evolving literature on rational agents enhanced by ML, and identifies key research opportunities and open challenges for designing effective rational ML agents.
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