提出知识可用性概念，帮助人机混合系统判断该向谁提问。
Knowledge Affordances for Hybrid Human-AI Information Seeking
- 用语义化的知识可用性描述信息源能回答什么问题
- 强调知识可用性由任务、偏好和情境共同决定
- 适合研究人机协作与智能搜索的学者参考
随着信息生态日益复杂，人类与人工智能代理面临一个简单却未解决的问题：在寻求知识时，该向谁询问，以及为何？受人类‘读空气’直觉启发，本文引入知识可用性（Knowledge Affordance, KA）概念，系统化地描述智能体如何在人机混合环境中识别有意义的信息获取机会。KA并非完整框架，而是以声明式、语义基础的方式描述知识源可提供的内容、适用问题类型及其上下文属性。我们还提出KA具有关系性，可能源于代理的任务、偏好与情境因素之间的互动。本研究的核心贡献是连接多个研究领域——包括可用性理论、语义网络服务、知识工程与查询、互理解性——提出一个概念性框架，并展望构建具备更高透明度、适应性和共享理解能力的KA感知系统的研究方向。
原文摘要 · Abstract (English)
As information ecosystems grow more heterogeneous, both humans and artificial agents increasingly face a simple yet unresolved question: when seeking knowledge, whom should we ask, and why? Inspired by how people intuitively "read a room", this paper introduces the concept of knowledge affordance (KA) to systematize how agents identify meaningful opportunities for information seeking in hybrid human-AI environments. Rather than introducing a fully formed framework, we propose KAs as declarative, semantically grounded descriptions of what a knowledge source can offer, for which kinds of questions, and with which contextual properties. Additionally, we suggest that KAs are relational, possibly emerging from the interplay between the agent's task, preferences and situational factors. Our contribution is thus a conceptual proposal that connects different research streams, including affordances, semantic web services, knowledge engineering and querying, and mutual intelligibility. We sketch possible research directions to build KA-aware systems that navigate information spaces with greater transparency, adaptability and shared understanding.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。