arXiv:2607.06214v1 Announce Type: new Abstract: This paper offers 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 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 ki
The increasing sophistication and autonomy of AI systems necessitate deeper theoretical frameworks for understanding and designing their motivational and learning processes, particularly concerning curiosity.
A robust framework for human-AI curiosity ecosystems could inform the development of more adaptable, efficient, and aligned AI agents, impacting their utility across various applications.
This theoretical work provides a new lens for considering how AI agents acquire information and interact with humans, potentially leading to novel designs for AI learning and human-AI collaboration.
- · AI researchers and developers
- · Robotics
- · AI-driven R&D sectors
- · Traditional, strictly goal-oriented AI development
- · Static AI systems
Improved understanding of AI learning policies and inquiry processes.
Development of more intrinsically motivated and adaptive AI agents capable of sustained discovery.
Enhanced human-AI collaboration in complex problem-solving domains due to more nuanced AI curiosity and communication.
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Read at arXiv cs.AI