提出好奇心生态框架，模拟单多智能体的探索行为与知识演化。
A framework for single and multi-agent human-AI curiosity ecosystems
- 将好奇心建模为权重重塑的决策系统，随经验动态调整提问策略。
- 揭示提问量、话题多样性与可复用知识等关键指标在协作探索中的演变规律。
- 适用于设计自驱动发现型多智能体AI系统，适合关注自主学习的研究者。
本文提出一个将好奇心视为生态系统的框架。首先，单个智能体的提问策略（何时、为何、如何提问）取决于其对即时不确定性降低、成本、延迟回报以及保持问题开放价值的权衡，且这些权重会随经验变化。例如，短时间内低成本快速回答的问题会改变短期提问成本，并影响长期更倾向于回答哪些问题。其次，该框架扩展至多个智能体共享知识景观的场景，追踪提问总量、话题多样性、前沿导向探索、冗余度及可复用知识等指标。该框架为研究好奇心生态提供概念基础，并指导未来面向发现任务的多智能体AI系统设计。
原文摘要 · Abstract (English)
This paper offers a framework for considering curiosity as an ecosystem. First, it suggests that a single agent's inquiry policy (how, when, and why an agent asks a question) depends on how the agent values immediate uncertainty reduction, costs, delayed return, and the value of keeping the question open. A key concept in the framework is that the weights on these decision-related terms can change with experience. For example, a period of cheap, quickly answered questions may change the cost of inquiry on a short timescale and change which kinds of questions the agent is drawn to answer over a longer timescale. Second, these ideas are extended to many agents exploring a shared knowledge landscape, and there the framework tracks inquiry volume, topic diversity, frontier-directed inquiry, redundancy, and reusable knowledge. The result is a conceptual framework for studying curiosity ecology and for future efforts towards designing multi-agent AI systems for discovery.
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