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、意外情况的人工智能方法是一个尚未解决的难题。推动新颖性适应技术发展的挑战之一，在于缺乏用于评估模型在陌生场景下性能的测试框架。近年来，在《科学鸟》和《大富翁》等领域中采用的新颖性生成方法，通过在搜索过程中利用人类领域专业知识来发现新异情境。此类方法在新颖性生成前引入人类指导，并能产出可直接加载到模拟环境中的新异场景。我们提出一种新颖性生成新方法，该方法使用环境的抽象模型（包括仿真领域），无需依赖领域特定的人类指导即可生成新异场景。其关键成果在于能够生成更大（通常为无限）的新颖性空间，但代价是需要引入人工指导来后置筛选和过滤新异场景。我们通过开源的新颖性生成库描述了人机协同的新颖性生成流程，并在《大富翁》和VizDoom两个领域中对基线智能体进行了测试。结果表明，人机协同方法使用户能在4小时内为《大富翁》和VizDoom领域完成新颖性的开发、实现、测试与修订。