Developing artificial intelligence approaches to overcome novel, unexpected circumstances is a difficult, unsolved problem. One challenge to advancing the state of the art in novelty accommodation is the availability of testing frameworks for evaluating performance against novel situations. Recent novelty generation approaches in domains such as Science Birds and Monopoly leverage human domain expertise during the search to discover new novelties. Such approaches introduce human guidance before novelty generation occurs and yield novelties that can be directly loaded into a simulated environment. We introduce a new approach to novelty generation that uses abstract models of environments (including simulation domains) that do not require domain-dependent human guidance to generate novelties. A key result is a larger, often infinite space of novelties capable of being generated, with the trade-off being a requirement to involve human guidance to select and filter novelties post generation. We describe our Human-in-the-Loop novelty generation process using our open-source novelty generation library to test baseline agents in two domains: Monopoly and VizDoom. Our results shows the Human-in-the-Loop method enables users to develop, implement, test, and revise novelties within 4 hours for both Monopoly and VizDoom domains.

翻译：开发能够应对新颖、意外情境的人工智能方法是一个困难且尚未解决的问题。推动新奇适应领域发展的挑战之一，在于缺乏可用于评估系统在面对新情境时性能的测试框架。近年来的新奇性生成方法（如应用于Science Birds和Monopoly领域）在搜索过程中借助人类领域专业知识来发现新的新奇性。这类方法在新奇性生成之前引入人类指导，并产出可直接加载到模拟环境中的新奇性。我们提出了一种新颖的新奇性生成方法，该方法利用环境的抽象模型（包括模拟领域），无需依赖领域的人类指导即可生成新奇性。其关键结果是可以生成更大、往往是无限的新奇性空间，但代价是需要在新奇性生成后引入人类指导来筛选和过滤。我们描述了基于人机回环的新奇性生成流程，并使用开源新奇性生成库在两个领域（Monopoly和VizDoom）中测试基线智能体。实验结果表明，在Monopoly和VizDoom领域中，人机回环方法使得用户能够在4小时内完成新奇性的开发、实施、测试和修订。