As more and more AI agents are used in practice, it is time to think about
how to make these agents fully autonomous so that they can learn by themselves
in a self-motivated and self-supervised manner rather than being retrained
periodically on the initiation of human engineers using expanded training data.
As the real-world is an open environment with unknowns or novelties, detecting
novelties or unknowns, characterizing them, accommodating or adapting to them,
gathering ground-truth training data, and incrementally learning the
unknowns/novelties are critical to making the agent more and more knowledgeable
and powerful over time. The key challenge is how to automate the process so
that it is carried out on the agent's own initiative and through its own
interactions with humans and the environment. Since an AI agent usually has a
performance task, characterizing each novelty becomes critical and necessary so
that the agent can formulate an appropriate response to adapt its behavior to
accommodate the novelty and to learn from it to improve the agent's adaptation
capability and task performance. The process goes continually without
termination. This paper proposes a theoretic framework for this learning
paradigm to promote the research of building Self-initiated Open world Learning
(SOL) agents. An example SOL agent is also described.

该论文提出了一个理论框架来推动构建自我发起的开放世界学习（SOL）代理的研究，讨论和解决了在实现完全自主可学习代理方面的挑战，特别是如何自动化地检测未知的情况或新奇情况，并适应或调整代理以从中学习，提高代理的适应能力和任务性能。