Autonomous agents acting in real-world environments often need to reason with unknown novelties interfering with their plan execution. Novelty is an unexpected phenomenon that can alter the core characteristics, composition, and dynamics of the environment. Novelty can occur at any time in any sufficiently complex environment without any prior notice or explanation. Previous studies show that novelty has catastrophic impact on agent performance. Intelligent agents reason with an internal model of the world to understand the intricacies of their environment and to successfully execute their plans. The introduction of novelty into the environment usually renders their internal model inaccurate and the generated plans no longer applicable. Novelty is particularly prevalent in the real world where domain-specific and even predicted novelty-specific approaches are used to mitigate the novelty's impact. In this work, we demonstrate that a domain-independent AI agent designed to detect, characterize, and accommodate novelty in smaller-scope physics-based games such as Angry Birds and Cartpole can be adapted to successfully perform and reason with novelty in realistic high-fidelity simulator of the military domain.

翻译：在真实环境中运行的自主智能体时常需要应对干扰其计划执行的未知新颖性现象。新颖性是一种可能改变环境核心特征、组成和动态的意外现象。在任意足够复杂的环境中，新颖性可能在任何时刻发生，且不会提前预警或提供解释。先前研究表明，新颖性对智能体性能具有灾难性影响。智能体通过内部世界模型进行推理，以理解环境的复杂性并成功执行其计划。新颖性引入环境后，通常会导致其内部模型失准，生成的计划不再适用。在真实世界中，新颖性尤为普遍，常采用领域特定甚至预测性的新颖性特定方法来缓解其影响。本研究证明，在《愤怒的小鸟》和《小推车》等小规模物理游戏中设计用于检测、表征和接纳新颖性的领域无关AI智能体，可被成功适配至现实军事领域的高保真模拟器中，使其在面临新颖性情境时仍能有效执行任务与推理。