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.

翻译：随着越来越多的人工智能智能体投入实际应用，我们有必要思考如何使这些智能体完全自主化，使其能够以自我激励和自我监督的方式自行学习，而非依赖于人类工程师定期使用扩展训练数据对其进行重新训练。由于现实世界是一个存在未知或新颖性的开放环境，因此检测新异事物、对其进行表征、容纳或适应它们、收集真实标注训练数据，以及增量式学习这些未知/新颖内容，对于使智能体随时间推移变得更加知识渊博且能力强大至关重要。其中的关键挑战在于如何自动化这一过程，使其由智能体主动发起，并通过其自身与人类及环境的交互来完成。鉴于AI智能体通常承担特定性能任务，对每种新异事物进行表征变得关键且必要，以便智能体能够制定适当的响应策略，调整行为以适应新异事物并从中学习，从而提升其适应能力与任务性能。这一过程将持续进行，永不停歇。本文为该学习范式提出了一个理论框架，以推动构建自主启动的开放世界学习（SOL）智能体的研究。文中还描述了一个示例性的SOL智能体。