AI Autonomy: Self-Initiation, Adaptation and Continual Learning

Abstract

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 selfmotivated 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, detecting novelties, characterizing them, accommodating or adapting to them, and gathering ground-truth training data and incrementally learning the unknowns/novelties are critical to making the AI agent more and more knowledgeable and powerful over time. The key challenge 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.

UER: A Heuristic Bias Addressing Approach for Online Continual Learning

“…Such a phenomenon is named catastrophic forgetting (CF) [12]. Since solving this problem is of great significance, continual learning [25][26][27] has been proposed to train the model to achieve knowledge accumulation without forgetting. Particularly, online continual learning (OCL) is a realistic scenario of continual learning.…”