AI自主性：自主发起的开放世界持续学习与适应
AI Autonomy : Self-Initiated Open-World Continual Learning and Adaptation
AI总结：
本文针对开放世界中AI智能体需自主持续学习并适应新颖环境的问题，提出SOLA框架，并通过实现的智能体验证可行性。
摘要：
随着越来越多的AI智能体在实践中得到应用，现在是时候思考如何使这些智能体实现完全自主，从而能够：（1）以自我激励、自主发起的方式持续自行学习，而不是由人类工程师发起并定期进行离线重训练；（2）容纳或适应意外或新颖的情况。由于现实世界是一个充满未知或新事物的开放环境，检测新事物、刻画其特征、容纳/适应它们、收集真值训练数据并增量学习未知/新事物的能力，对于使AI智能体随时间推移变得越来越知识丰富、强大和自我维持至关重要。这里的关键挑战是如何使该过程自动化，使其由智能体自主发起，并通过其与人类、其他智能体和环境的交互持续执行，就像人类的在岗学习一样。本文为这种学习范式提出了一个名为SOLA的框架，以推动构建具备自主和持续学习能力的AI智能体的研究。为展示可行性，文中还描述了一个已实现的智能体。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
