Self-Initiated Open World Learning for Autonomous AI Agents

TL;DR

This paper tackles how autonomous AI agents can operate effectively in open-world settings by enabling self-initiated, continual learning after deployment. It introduces Self-Initiated Open World Learning (SOL), a framework where a primary task is supported by modules L (learner), E (executor), N (novelty characterizer), and I (interactive) to detect novelties, characterize them, gather ground-truth data, and incrementally update knowledge. The authors formalize data-shift concepts—including covariate shift, prior probability shift, and concept drift—and define a novelty score $(h(x), h(D_{tr}))$ relative to training data $D_{tr}$ and known classes $Y_{tr}$ to enable automatic novelty detection and potential expansion to new classes $y_{new}$. An instantiation with a CML dialogue system demonstrates the approach, and the paper discusses challenges such as few-shot continual learning, risk management, and knowledge revision, arguing that SOL is a path toward more capable, autonomous agents in dynamic environments.

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 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.