This paper presents a toy 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 returns, 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 influence which types of questions the agent is inclined to ask over a longer timescale.
Second, these ideas are extended to many agents exploring a shared knowledge landscape, where the framework tracks inquiry volume, topic diversity, frontier-directed inquiry, redundancy, and reusable knowledge.
The result is a conceptual toy framework for studying curiosity ecology and for future efforts towards designing multi-agent AI systems for discovery. It serves as a companion piece for a paper currently under review in Trends in Neurosciences.
Blogger's Review: This framework offers a fresh perspective on understanding agent behavior driven by curiosity, especially in multi-agent environments, effectively tracking and optimizing the knowledge exploration process. Future research can build on this framework to enhance AI systems' autonomous learning capabilities in complex settings.