This paper introduces the concept of knowledge affordance (KA) to help agents identify opportunities for information seeking in hybrid human-AI environments. KAs are described as declarative, semantically grounded descriptions of what a knowledge source can offer, for what types of questions, and under which contextual properties. The authors propose that KAs are relational and emerge from the interaction between an agent's task, preferences, and situational factors, aiming to foster greater transparency and adaptability in information navigation. AI
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IMPACT Introduces a conceptual framework for AI agents to better navigate and seek information in complex environments.
RANK_REASON This is a research paper introducing a new conceptual framework for AI information seeking.