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:利用人工智能(AI)剂的一个关键因素是,利用人工智能(AI)的代理商是否强大,是其新颖性的一个关键因素。人工智能(AI)代理商包括了设计或培训的模型。工程师模型包括了对环境方面知识的知识,这些方面是工程师所知道和认为重要的。根据培训数据所建立的联系,这些模型构成环境各方面的嵌入。然而,在运作过程中,一个丰富的环境可能会带来在培训组合中看不到的挑战,或者在设计模型中解释出的挑战。更糟糕的是,敌对环境可能会被对手改变。国防高级研究项目机构(DARPA)的一个方案寻求发展必要的科学,以开发和评价对新颖性具有活力的代理商。在AI具备在任务关键环境中所设想的作用之前,需要这种能力。作为AI和学会的科学和为开放世界新颖性(SAILON)学习的一部分,我们正在将可能的军事领域新颖性新颖性类型描述为新颖理论的一部分。从新颖的数学和科学上看,将使我们能够对来自DARPA的代理设计进行实验技术的实验设计进行实验。在实验室中,我们能够对新式的实验环境进行测试。</s>