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 (1) learn by themselves continually in a self-motivated and self-initiated manner rather than being retrained offline periodically on the initiation of human engineers and (2) accommodate or adapt to unexpected or novel circumstances. As the real-world is an open environment that is full of unknowns or novelties, the capabilities of detecting novelties, characterizing them, accommodating/adapting to them, gathering ground-truth training data and incrementally learning the unknowns/novelties become critical in making the AI agent more and more knowledgeable, powerful and self-sustainable over time. The key challenge here is how to automate the process so that it is carried out continually on the agent's own initiative and through its own interactions with humans, other agents and the environment just like human on-the-job learning. This paper proposes a framework (called SOLA) for this learning paradigm to promote the research of building autonomous and continual learning enabled AI agents. To show feasibility, an implemented agent is also described.

翻译：随着越来越多的AI代理在实践中使用，我们应当思考如何使这些代理完全自主，以便它们可以（1）以自我激励和自发的方式持续学习，而不是在人类工程师的启动下定期进行离线重新培训，以及（2）适应或适应于意外或新奇情况。由于现实世界是一个充满未知或新颖事物的开放环境，因此检测新颖性、表征新颖性、适应/调整到新颖性、收集地面真实数据并增量学习未知事物/新颖性的能力变得至关重要，以使AI代理越来越具有知识、能力和自我可持续性。关键挑战在于如何自动化这个过程，使其像人类在职学习一样，在代理自己的主动行动和与人类、其他代理和环境的交互中不断进行。本文提出了一个框架（称为SOLA），用于促进构建自主和持续学习的AI代理的研究。为了展示可行性，还描述了一个实现的代理。