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智能体在实际场景中得到应用，是时候思考如何使这些智能体完全自主化，使其能够：（1）以自我驱动和自我发起的方式持续自主学习，而非依赖人类工程师定期发起离线重新训练；（2）适应或应对意外或新奇情况。由于现实世界是一个充满未知或新奇事物的开放环境，检测新奇事物、对其进行表征、适应/接纳它们、收集真实标注训练数据以及增量学习未知/新奇事物的能力，对于使AI智能体随时间推移越来越博闻强识、强大且自我维持变得至关重要。这里的关键挑战在于如何自动化该过程，使其像人类在岗学习一样，通过智能体自身与人类、其他智能体及环境的交互，以自主发起的方式持续进行。本文提出一个名为SOLA的框架，用于推动构建具备自主与持续学习能力的AI智能体的研究。为证明可行性，还描述了一个已实现的智能体实例。