稳态持续学习
Homeostatic Continual Learning
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中文总结 AI 辅助
本文提出稳态持续学习，通过检测环境数据异常值使AI代理在变化环境中持续学习而不遗忘，并逐步完善模型与策略，同时探讨其构建世界模型的应用与影响。
中文摘要 AI 辅助
本文中，我阐述了一个持续学习问题，并提出了一种名为“稳态持续学习”的方法，使AI代理能够在变化的环境中持续学习，而不会发生灾难性遗忘。该方法的核心是，当代理在其输出中遇到异常值时，在环境数据中寻找异常值。通过这种方法，代理逐步完善其模型和策略，并在越来越多的情境中表现良好。我还建议，我们可以使用该方法构建一个世界模型，其中代理将世界中的对象分解为特征，将抽象对象抽象为概念的实例，并通过特征将概念映射到意图。我讨论了使该方法实用化所需的工作、该方法与人工智能多个领域的联系，以及该方法更广泛的影响。
英文摘要
In this paper, I formulate a Continual Learning problem and propose a method named "Homeostatic Continual Learning" that enables an AI agent to learn continuously in a changing environment without catastrophic forgetting. The core of the method is to find outliers in the environment data when the agent experiences an outlier in its output. Through this method, the agent gradually completes its model and policy and performs well in more and more contexts. I also suggest that we may use the method to build a world model where the agent factorizes the objects in the world into features, abstract objects into comparable instances of concepts and map concepts to intents through features. I discuss the works needed to render the method practical, the connections to many fields in Artificial Intelligence and the broader implications of the method.
发表机构
- Nokia Bell Labs France（诺基亚贝尔实验室法国）
机构由 AI 辅助整理，请以论文原文为准。
