  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.
