让AI更懂人：通过反思机制提升人机协作决策的准确与可信度。
Human-Centric Reflective Architecture for Human-AI Collaborative Decision-Making
- 将人机协作建模为博弈，用语言反馈驱动迭代优化
- 在真实任务中显著提升决策效果与推荐质量
- 适合需要高可信度的人机协同场景，如医疗、安全
大型语言模型（LLMs）被广泛应用于从日常任务到高风险关键应用的各类人类活动中，旨在以最少的人类干预提升决策效率，并使决策结果符合人类预期、偏好与需求，同时降低因AI不确定性带来的风险。然而，人类常对AI建议过度依赖或轻视，现有AI系统也未能有效匹配人类预期。为此，本文提出一种以人为本的人机协作决策框架，旨在增强人类能力并使AI代理与人类偏好保持一致。具体而言，本文（a）将协作决策任务形式化为AI代理与人类玩家之间的随机博弈；（b）提出人类中心反射架构（HCRA），结合人类校准模型与基于语言反馈的强化学习代理，实现迭代式反思过程。评估结果表明，HCRA能显著提升决策有效性并生成高质量推荐。
原文摘要 · Abstract (English)
The use of Large Language Models (LLMs) across diverse areas of human activity-ranging from everyday tasks to safety-critical applications-aims to enhance decision-making effectiveness with minimal human feedback. Concurrently, it seeks to align decisions with human expectations, preferences, and needs while mitigating risks associated with AI non-determinism. However, humans frequently over- or under-rely on AI recommendations, and current AI systems remain poorly calibrated to human expectations. To address these challenges, we introduce a human-AI collaborative decision-making framework designed to augment human capabilities and align AI agents with human preferences and expectations. Specifically, this paper (a) formulates the collaborative decision-making task as a stochastic game between an AI agent and a human player, and (b) proposes the Human-Centric Reflective Architecture (HCRA), which integrates human-calibrated models with reinforcement learning agents that leverage linguistic feedback in an iterative, reflective process. Evaluation results demonstrate that HCRA enhances decision-making effectiveness and delivers high-quality recommendations.
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