Current AI systems are designed to solve close-world problems with the
assumption that the underlying world is remaining more or less the same.
However, when dealing with real-world problems such assumptions can be invalid
as sudden and unexpected changes can occur. To effectively deploy AI-powered
systems in the real world, AI systems should be able to deal with open-world
novelty quickly. Inevitably, dealing with open-world novelty raises an
important question of novelty difficulty. Knowing whether one novelty is harder
to deal with than another, can help researchers to train their systems
systematically. In addition, it can also serve as a measurement of the
performance of novelty robust AI systems. In this paper, we propose to define
the novelty reaction difficulty as a relative difficulty of performing the
known task after the introduction of the novelty. We propose a universal method
that can be applied to approximate the difficulty. We present the
approximations of the difficulty using our method and show how it aligns with
the results of the evaluation of AI agents designed to deal with novelty.

本文提出一种评估 AI 系统应对开放世界新颖问题难度的方法，以帮助研究人员系统地训练其系统，并衡量其表现。