探索机器如何表达不确定性，区分数据和系统自身的不确定状态。
Can machines be uncertain?
- 从行为角度分析符号、连接和混合模型如何容纳不确定性
- 提出主观不确定性可表现为疑问态度而非命题判断
- 适合研究认知模型与可信AI的学者参考
本文从功能主义和行为视角探讨人工智能系统能否实现不确定性状态。区分了源于数据或信息的客观不确定性（epistemic uncertainty）与系统自身表现出的主观不确定性（subjective uncertainty）。进一步将主观不确定性分为分布式与离散式两种实现方式。核心贡献在于提出某些不确定性状态本质上是疑问态度，其内容为问题而非命题，揭示了机器不确定性在认知层面的新可能。
原文摘要 · Abstract (English)
The paper investigates whether and how AI systems can realize states of uncertainty. By adopting a functionalist and behavioral perspective, it examines how symbolic, connectionist and hybrid architectures make room for uncertainty. The paper distinguishes between epistemic uncertainty, or uncertainty inherent in the data or information, and subjective uncertainty, or the system's own attitude of being uncertain. It further distinguishes between distributed and discrete realizations of subjective uncertainty. A key contribution is the idea that some states of uncertainty are interrogative attitudes whose content is a question rather than a proposition.
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