A critical factor in utilizing agents with Artificial Intelligence (AI) is their robustness to novelty. AI agents include models that are either engineered or trained. Engineered models include knowledge of those aspects of the environment that are known and considered important by the engineers. Learned models form embeddings of aspects of the environment based on connections made through the training data. In operation, however, a rich environment is likely to present challenges not seen in training sets or accounted for in engineered models. Worse still, adversarial environments are subject to change by opponents. A program at the Defense Advanced Research Project Agency (DARPA) seeks to develop the science necessary to develop and evaluate agents that are robust to novelty. This capability will be required, before AI has the role envisioned within mission critical environments. As part of the Science of AI and Learning for Open-world Novelty (SAIL-ON), we are mapping possible military domain novelty types to a domain-independent ontology developed as part of a theory of novelty. Characterizing the possible space of novelty mathematically and ontologically will allow us to experiment with agent designs that are coming from the DARPA SAIL-ON program in relevant military environments. Utilizing the same techniques as being used in laboratory experiments, we will be able to measure agent ability to detect, characterize, and accommodate novelty.

翻译：人工智能智能体能否应对新颖性是决定其在任务关键环境中发挥作用的关键因素。人工智能智能体包含工程化模型和训练模型两类：工程化模型整合了工程师认为已知且重要的环境要素知识，而训练模型则基于训练数据间的关联形成环境要素的嵌入表示。但在实际运行中，复杂环境常会呈现训练集未涵盖或工程化模型未预见的挑战。更严峻的是，对抗性环境可能因对手的干预而持续变化。美国国防高级研究计划局正推进一项计划，旨在发展评估与构建能应对新颖性的智能体所需的基础科学。这项能力是人工智能在任务关键环境中发挥预期作用的前提条件。作为"开放世界新颖性人工智能与学习科学"项目的一部分，我们正将军事领域可能的新颖性类型映射至基于新颖性理论构建的领域无关本体。通过数学与本体论维度对新颖性空间进行特征描述，我们得以在相关军事场景中测试DARPA SAIL-ON项目研发的智能体设计方案。采用与实验室实验相同的研究方法，我们将能量化评估智能体在检测、表征与适应新颖性方面的能力。