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.

翻译：得益于机器学习（ML）模型在感知与认知任务中展现出的卓越类人能力，将ML整合至理性智能体架构的框架正日益受到关注。然而，该领域的研究仍呈现碎片化且缺乏系统性，往往侧重于将ML嵌入通用智能体容器，而忽视了理性架构（如信念-欲望-意图（BDI）智能体）的表达能力。本文以BDI范式为参照，对现有方法进行了细粒度的系统化梳理。我们的分析展示了由ML增强的理性智能体领域快速发展的研究现状，并指出了设计高效理性ML智能体的关键研究机遇与开放挑战。