发表机构
Johns Hopkins University（约翰斯·霍普金斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该论文提出一个玩具框架，将好奇心视为生态系统，探讨单智能体询问策略及相关决策项权重随经验的变化，还扩展到多智能体，追踪多种指标，为研究好奇心生态及设计多智能体发现系统提供概念框架。
AI 中文摘要
本文提供了一个将好奇心视为生态系统的玩具框架。首先，提出单智能体的询问策略（智能体如何、何时以及为何提问）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的价值的重视程度。框架中的关键概念是这些与决策相关的项的权重会随经验变化。例如，一段低成本、快速得到答案的时期可能在短时间内改变询问成本，并在较长时间内改变智能体倾向回答的问题类型。其次，这些想法扩展到多个探索共享知识领域的智能体，框架追踪询问量、主题多样性、前沿导向询问、冗余性和可复用知识。结果是一个用于研究好奇心生态以及未来设计用于发现的多智能体人工智能系统的概念性玩具框架。它是《神经科学趋势》中一篇正在审稿的论文的配套文章。
英文摘要
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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